knockout libraries for desirable traits of industrial applications … · production and screening...
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Production and screening of Nannochloropsis oceanica
knockout libraries for desirable traits of industrial
applications
Daniel Rodrigues Figueiredo
Thesis to obtain the Master of Science Degree in
Biotechnology
Supervisors: Professor Maria João Gante De Vasconcelos Barbosa and Professor
Isabel Maria De Sá Correia Leite de Almeida
Examination Committee
Chairperson: Professor Leonilde de Fátima Morais Moreira
Supervisor: Professor Isabel Maria De Sá Correia Leite de Almeida
Members of the Committee: Professor Rodrigo da Silva Costa
October, 2018
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Acknowledgements
First, I would like to express my sincere gratitude to my supervisors for accepting me to develop this
work – in Wageningen University & Research, Professor Maria Barbosa and Dr. Christian Südfeld, and
in Instituto Superior Técnico, Professor Isabel Sá-Correia. To Christian, for all the trust that was given
in the lab, for all the knowledge, patience, guidance, advices and for all the hours spent discussing data
and exchanging impressions, thank you so much. Words might not be enough to express how grateful
I am.
To all my lab friends and colleagues, especially to Léa, Sara, Jetzlin, Diana, Prissylia, Teije, Maria,
Bárbara and Filippo, for all the coffee-breaks, lunches, dinners, successes and failures we shared and
for all the amazing moments in Wageningen, thank you a lot. To all my good friends that usually do not
understand half of what I am saying when they ask me about work, thank you for all the incredible
moments. Finally, to my family – mom, grandmother, uncle, brother, sister and cousins – thank you for
everything, for all the sacrifices, all the belief, trust, patience and love. I am forever grateful to you.
To my grandfather, José Augusto Rodrigues, thank you for all the wisdom, discipline and guidance
throughout my life. To my grandmother, Bertulina Marques Figueiredo, thank you for all your care,
attention and love. This thesis is dedicated to you both.
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Abstract
The microalga Nannochloropsis oceanica is a high lipid producer with great relevance for food, feed and
biofuel industries. It is one of the most studied microalgae species and genetic engineering advances
have been already performed. However, its strain development is still hindered by a poor genome
annotation. This study has focused on the development of an efficient pipeline for producing, selecting
and screening Nannochloropsis oceanica IMET1 knockout libraries so that genes related to chlorophyll
and lipids could be identified. For mutant selection we report a novel approach using fluorescence
activated cell sorting. Results show that mutant cells were successfully sorted after one day of
transformation. However, only an efficiency of 36% was obtained, in comparison to selection in solid
media using antibiotic. A screening pipeline for finding altered traits of chlorophyll and lipids in a knockout
library are presented, as well as its physiological and technical limitations. Although some phenotypes
suggested altered traits in an intermediate screening step, differences were not significant after mutant
isolation. Factors such as fluorescence sample-to-sample variation, due to differences in cell density
and size, as well as low cell viability after staining and sorting are the major limitations. At last, a knockout
library with ¼ of genome coverage might also explain the apparent lack of interesting phenotypes.
Keywords: Nannochloropsis oceanica; Fluorescence-Activated Cell Sorting; Random Mutagenesis;
Mutant library, Altered phenotype
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Resumo
A microalga Nannochloropsis oceanica IMET1 produz lípidos em grande quantidade e tem aplicações
para a indústria alimentar e biocombustíveis. Embora esta espécie seja uma das microalgas mais
estudadas, incluindo avanços na engenharia genética, o desenvolvimento das suas estirpes é ainda
limitado pela falta de conhecimento de anotação do genoma. O objetivo desta tese é desenvolver uma
estratégia eficiente para produzir, selecionar e rastrear bibliotecas de mutantes IMET1 para alterações
fenotípicas, de modo a relacionar os genes bloqueados com as características resultantes. Neste
trabalho foi reportada uma nova técnica de seleção de transformantes com o uso de fluorescência e
separação celular. Os resultados indicam que após um dia de transformação é possível recolher células
mutantes com uso de Fluorescence Activated Cell Sorting. No entanto, não houve um enriquecimento
significativo de mutantes e apenas uma eficiência de 36% foi observada em comparação ao método
tradicional de seleção em antibiótico. Uma estratégia de rastreamento de mutantes foi desenvolvida
para identificar alterações na composição de clorofila e lípidos. No passo intermédio de rastreamento,
com o uso de microplacas, alguns resultados sugeriram alterações fenotípicas. No entanto, os
resultados das análises dos isolados não foram significativas. Os fatores que mais limitaram o
rastreamento e identificação destes fenótipos deram-se ao nível da variabilidade de amostra, devido às
diferenças de densidade e tamanho celular, bem como a perda de viabilidade após a coloração e cell
sorting. Por último, o uso de uma biblioteca com apenas ¼ da cobertura do genoma pode também
explicar a ausência de fenótipos relevantes.
Palavras-chave: Nannochloropsis oceanica; Citometria de fluxo com cell sorting; Mutagénese
aleatória; Biblioteca de mutantes; Alterações de fenótipo
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List of Acronyms
ACP Acyl carrier protein IC Insertional Cassette
AS After sorting ID Identifier
ASW Artificial Sea Water IQR Interquartile Range
ATP Adenosine triphosphate LB Lysogeny broth
BDP BODIPY mKO2 mKusabira-Orange2
BODIPY Boron-dipyrromethene NADP
H
Nicotinamide adenine
dinucleotide phosphate
BP Big Pool NB NutriBloom
BPD Big Pool Diluted NGS New Generation Sequencing
BS Before sorting NR Nile Red
BSC Back Scatter OD Optical Density
CFU Colony Forming Units PCR Polymerase Chain Reaction
CLPP Clp Protease PSI Photosystem I
DMSO Dimethyl Sulfoxide PSII Photosystem II
DNA Deoxyribonucleic Acid QY Quantum Yield
EDTA Ethylenediamine tetraacetic
acid RNA Ribonucleic Acid
EPA Eicosapentaenoic acid RUBIS
CO
Ribulose-1,5-bisfosfato
Carboxilase Oxigenase
EPS Events Per Second SD Standard Deviation
ER Endoplasmic Reticulum SSD Salmon Sperm DNA
FACS Fluorescence Activated Cell
Sorting TAG Triacylgliceride
FAS Fatty Acyl Synthase TATUB α-tubulin terminator
FCM Flow Cytometer TCDB Transporter Classification
Database
FSC Forward Scatter UNS Unstained
GFP Green Fluorescence Protein VCP Violaxanthin Chlorophyll-a
binding Protein
GFP Green Fluorescence Protein WT Wild Type
HEPES 4-(2-hydroxyethyl)-1-
piperazineethanesulfonic Acid
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Table of contents
Acknowledgements ...................................................................................................................................i
Abstract ..................................................................................................................................................... ii
Resumo ................................................................................................................................................... iii
List of Acronyms ...................................................................................................................................... iv
Table of contents ......................................................................................................................................v
List of Figures ......................................................................................................................................... vii
List of Tables ............................................................................................................................................x
1. Introduction ................................................................................................................................ 11
1.1. General Introduction .............................................................................................................. 11
1.2. Aim ......................................................................................................................................... 13
2. Theoretical Background ............................................................................................................. 14
2.1. Microalgae and its applications in biotechnology .................................................................. 14
2.2. Photosynthesis and lipid metabolism .................................................................................... 15
2.3. Molecular tools for microalgae ............................................................................................... 17
2.4. Microalgal culturing techniques ............................................................................................. 20
2.5. Fluorescence Activated Cell Sorting...................................................................................... 21
3. Materials and Methods .............................................................................................................. 25
3.1. Strain and growth conditions ................................................................................................. 25
3.2. Optical density and quantum yield measurements ................................................................ 25
3.3. Genomic constructs and transformation of N. oceanica IMET1 ............................................ 26
3.3.1. Plasmids design .................................................................................................................... 26
3.3.2. DNA preparation for transformation ...................................................................................... 28
3.3.3. Transformation of N. oceanica IMET1 for generating knockout libraries .............................. 28
3.4. Fluorescence Activated Cell Sorting...................................................................................... 28
3.5. Method Development Experiments ....................................................................................... 30
3.5.1. Optimization of electroporation parameters .......................................................................... 30
3.5.2. Cell viability after sorting and staining with BODIPY ............................................................. 30
3.6. Screening knockout libraries for altered profiles of lipids and chlorophyll ............................. 31
3.6.1. Mutant library preparation ..................................................................................................... 31
3.6.2. Screening the mutant library for lipids and chlorophyll altered phenotypes .......................... 32
3.7. A selection mutant approach using Fluorescence Activated Cell Sorting ............................. 33
3.7.1. Confirmation of IC16 – IC18 mutant phenotypes .................................................................. 33
3.7.2. Selecting IC18 mutant knockouts using FACS ..................................................................... 33
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4. Results and Discussion ............................................................................................................. 34
4.1. Method Development Experiments ............................................................................................. 34
4.1.1. Optimization of the transformation protocol .......................................................................... 34
4.1.2. Viability Assessment of staining and sorting using the FACS ............................................... 37
4.2. Screening a knockout mutant library for altered chlorophyll and lipid contents .................... 40
4.3. Development of a fluorescence – based selection for IMET1 transformants ........................ 52
4.3.1. Construction of IC16, IC17 and IC18 and screening for high fluorescence .......................... 52
4.3.2. A mutant selection approach based on fluorescence of IC18............................................... 55
5. Conclusion and future perspectives .......................................................................................... 61
6. References ................................................................................................................................ 62
7. Supplementary Material ............................................................................................................. 69
7.1. Supplementary File 1. PCR settings and purification results of the 3 genomic constructs
used for ligation of all plasmids .......................................................................................................... 69
7.2. Supplementary File 2. PCR amplification settings and purification for all insertional cassettes
70
7.3. Supplementary File 3. Screening experiments – microplate layout used for sorting ............ 73
7.4. Supplementary File 4. Screening experiments – example of plate layout for cell isolation .. 74
7.5. Supplementary File 5. Fluorescence- based selection – Gates applied for cell sorting ........ 75
7.6. Supplementary File 6. Screening Experiments – Sample identifiers of analysed microplate
wells 76
7.7. Supplementary File 7. Screening Experiments – Sample identifiers of isolated events from
microplate-wells .................................................................................................................................. 78
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List of Figures
Figure 1. Schematic representation of photosynthesis and lipid synthesis. Energy is passed through
the reaction centres (PSII and PSI), therefore reaching the dark phase, where RUBISCO fixes carbon
dioxide to form carbohydrates in Calvin Cycle. These C3 molecules are then used to synthetize lipids,
preferably triacylglycerides (TAGs) that may be used to produce biodiesel. Yellow arrows symbolize the
reactions that could be maximized, while the losses in purple arrows should be minimized. Retrieved
from Stephenson et al. (2011).
Figure 2. Variation in the genome of different N. oceanica strains in terms of gene functionality. Adapted
from D. Wang et al. (2014)
Figure 3. Schematic diagram concerning the working mechanism of droplet cell sorting in the FACS (a).
Image of a typical flow stream at the droplet break-off point (b). Adapted from Robert A. Andersen,
(2006).
Figure 4. Sorting modes of the SH800 Cell sorter (Sony Corporation®) for the different sorting chips.
Adapted from Cell Sorter Operator’s Guide, retrieved from https://danstem.ku.dk/images-new-
website/LE-SH800_Operators_Guide_EN_Rev10.pdf.
Figure 5. General structure of the 4 plasmids. Backbone comprises the ampicillin resistant gene and E.
coli origin of replication, used for E. coli cloning steps. Insertional cassette of all plasmids contains the
VCP promoter and intron splicer, differ at the selection markers and have the same terminators (alpha
tubulin - TATUB, and CLP protease (CLPP)). Additional features such as the P2A sequence and a
random 3 prime sequence was not represented. Furthermore, the fragment sizes here represented do
not correspond to the real scale in terms of base pair length.
Figure 6. Gate hierarchy used in the Sony cell sorter software. Forward and Back Scatter detectors are
first used to plot all events. Standard gates: Cells, FSC singlets, BSC singlets. The latter is used for
visualizing fluorescence data on fluorescence channels (FL1 – FL6). All event plot and FL1 to FL6 plots
are plotted in logarithmic scale.
Figure 7. Experimental set-up for preparing a knockout mutant library in level 2 and level 3 pools in
liquid media. Flushing step – from agarose plates to tubes in liquid media containing 1000 mutants; level
1 pools – for creating backups and level 2 pools; level 2 pools – for creating level 3 pools and both used
for screening experiments.
Figure 8. Optimization of electroporation parameters. (A) – exponential and late exponential growth
stage of cultures, n=8; (B) – cell concentration in the electroporation cuvettes, n=2; (C) – concentration
of DNA per sample, n=4; (D) – quantity of SSD times DNA template concentration, n=2; (E) – influence
of recovery medium temperature, n=4; (F, G) – DMSO pre-treatment (% v/v – volume of pure DMSO
per total volume in the cuvette), with and without SSD, n= 2 and n=4, respectively. All error bars are
given in standard deviation (SD).
viii
Figure 9. Flow-scheme of the viability experiment. Samples can be identified for the number of sorting
rounds as S1, S2 or S3, and the staining condition as BDP (BODIPY stained) or UNS for unstained.
Plate A (S1_UNS) was done by sorting tube 1. Plate B (S2_UNS) derived from tube 2 (the latter sorted
from tube 1). Plate C (S3_UNS) derived from tube 3, following the same logic. Plate D (S1_BDP) derived
from tube 4, previously stained with BODIPY. A tube 5 was made for plate E (S2_BPD) and a stained
version of it was performed for plate F (S2_BDP2), the latter with a second round of staining (tube 6).
Figure 10. Cell viability on agarose plates after multiple rounds of cell sorting and BODIPY staining.
Sorting was applied at ultra-purity mode and plate G, without antibiotic, was performed in the same
conditions as plate A. Efficiency was calculated based on obtained CFU numbers and total sorted events
(n=196).
Figure 11. Schematic representation of the mutant library screening for altered profiles of lipids and
chlorophyll.
Figure 12. Gates designed for screening chlorophyll and lipid altered phenotypes of the mutant library.
Y axis on both A and B were plotted in logarithmic scale. Gate percentages were adjusted in every
sample to achieve a close percentage of 5%, 0.5% and 0.05%, low or top percentages.
Figure 13. Experimental flow scheme of altered phenotypes confirmation and isolation. Step 1 –
microplate incubation for 2 weeks, followed by media replacement of Day1 and Day2 plates for nitrogen
starvation. Step 2 – mutant phenotype analyses for confirming altered profiles in each sample well. Step
3 – mutant sorting and isolation in single cell mode of confirmed phenotypes.
Figure 14. Flow cytometry analyses of microplate sorted events for chlorophyll altered phenotypes.
Plotted data are the median values of FL5 channel. Error bars correspond to the interquartile range
(IQR; 50% for the positive and 50% for the negative).
Figure 15. Flow cytometry analyses of microplate sorted events for lipid altered phenotypes. Plotted
data are the median values of FL1 channel. Error bars correspond to the interquartile range (IQR; 50%
for the positive and 50% for the negative).
Figure 16. Influence of staining with BODIPY in the FL1 channel of a microplate sample screened for
lipid analyses. A “tail” is observed in the black square with events at lower green fluorescence. Data is
plotted for the FL1-A in the X axis logarithmically
Figure 17. Chlorophyll events over forward scatter in phenotypes of microplate screened samples in
Day0.
Figure 18. Flow cytometer analyses of cells isolated from interesting phenotypes of microplates in Day0
chlorophyll and lipids. All samples in chlorophyll relate to sorted high fluorescent phenotypes. In lipids,
samples 17 and 4 relate to sorted high sorted and samples 2 to 5 for low sorted phenotypes.
Figure 19. Influence of cell density on chlorophyll and lipid fluorescence spectra of WT cells. (+) little
cell material; (++) medium cell material; (+++) big amount of cell material. Dilutions of 10-fold were
made for every sample condition.
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Figure 20. Structure of Insertional Cassettes 16, 17 and 18, containing fluorescent proteins mKO2,
mCherry and GFP, respectively.
Figure 21. Fluorescence distribution of IC16, IC17 and IC18 isolated colonies compared to a wild-type
control.
Figure 22. Colony PCR results of IC16, IC17 and IC18 colonies. Digestion and amplification with Phire
polymerase kit. One kilobase ladder was loaded in the 1% agarose gel on both ends.
Figure 23. Experimental flow scheme of a fluorescence-based approach for selection of IMET1
transformants
Figure 24. GFP mutant selection results after two rounds of sorting using the FACS – Sorting round 1
(S1) and Sorting round 2 (S2).
Figure 25. FL1 and FL2 channels before and after sorting, analysed in the day of sorting round 1 and
the day after, relative to sorting 2, respectively.
Figure 26. Green fluorescence variation on samples screened before and after sorting. Before sorting
(BS) was analysed on the day of the first round; after sorting (AS) samples were analysed the day after,
before sorting round 2. The numeric value 1 and 2 relate to the time of sample analyses, being samples
1 screened one to two hours before samples 2.
Figure 27. DNA amplification of sh ble gene, backbone and mcherry used for Gibson assembly.
Figure 28. DNA amplification of IC16, IC17 and IC18.
Figure 29. Amplification of IC21 with both Phire and Q5 polymerases. A faint band in sample L was due
to a mistake while loading the gel.
Figure 30. Microplate sorting layout for lipids and chlorophyll fluorescence events in screening Day 0
(A), and Day1 and Day (B) pools. L, M and H from the first letter stands for low, medium and high,
respectively. Second letters L or C stand for lipids and chlorophyll, respectively. Sorted populations of
5%, 0.5% and 0.05% of respective population area were sorted based on FL1 or FL5 plotted against
FSC-A. Images retrieved from Cell Sorter Software (Sony Corporation ®).
Figure 31. Example of a 384-well agarose plate used for cell isolation of interesting phenotypes related
to high chlorophyll Day1 pools. D1 relates to the starvation day, the second identifier to the pool name
and the third, the microplate analysed well. Events were sorted in single-cell-mode using the FACS.
Figure 32. Gate used for isolation of high fluorescence events in the FL1 channel respective to the
sorting round 1. (A) Gate High Fluorescence (HF) comprising 2.5% of the top fluorescence population
without doublets and triplets. (B) Doublets and triplets gate comprising ~30%.
Figure 33. Gate used for isolation of high fluorescence events in the FL1 channel respective to the
sorting round 2. Gate High Fluorescence (HF) comprising 5% of the top fluorescence population without
accounting doublets and triplets.
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List of Tables
Table 1. General characteristics of the designed plasmids. Name of plasmids and insertional cassettes,
selection markers, additional features and length of the constructs.
Table 2. Flow cytometer fluorescence channels and its biological interpretation
Table 3. Sorting and staining conditions used in the cell viability experiment.
Table 4. Purification of the genomic constructs sh ble, backbone and mcherry, previously amplified.
Table 5. Purification results of IC16, IC17 and IC18 with the Thermofisher purification kit.
Table 6. Optimization of the IC21 DNA purification using two polymerases and two purification kits.
Table 7. Sample identifiers of microplate sorted-mutants screened for altered chlorophyll profiles.
Table 8. Sample identifiers of microplate sorted-mutants screened for altered lipid profiles.
Table 9. Sample identifiers of the isolated events from previous microplate wells. The sample ID relates
to the sample number of the microplate analyses. Screening of isolated cells was solely performed for
Day0.
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1. Introduction
1.1. General Introduction
Over the past years climate change has been one of the major concerns for humanity. Since fossil fuels
are still a major resource for many applications, the urgency for greener alternatives is real. Microalgae
are photosynthetic microorganisms with great potential to produce valuable products in a very
sustainable way. Applications include food and feed, cosmetics, nutraceutical, pharmaceutical and
bioenergy industries, either as whole cells or as proteins, lipids or pigment extractions. Microalgae can
be produced in non-arable land therefore not competing with other traditional crops. When cultivated
photo-autotrophically it uses CO2, which could be derived from fossil fuel-fired power plants (Hu et al.,
2008). Algae can be cultivated in water with poor quality and research regarding its use in wastewater
treatment has been widely performed (Zhou, Yuan, Chen, & Ochieng, 2015). In lipid production
microalgae efficiently synthesize and accumulate large quantities of neutral lipids (20 to 40% of dry cell
weight), namely triacylglycerides (TAGs), and these biomolecules are suitable for biodiesel conversion
(Gong & Jiang, 2011). Furthermore, microalgae could be 10 to 20 times more efficient comparing to
traditional crops at producing biodiesel (Gouveia & Oliveira, 2009).
Although production of biodiesel from microalgae is already technically feasible, its economics is
hindered by high costs of production and raw materials (Gong & Jiang, 2011). One of the most important
features that strongly outlines the cost of a bioprocess is the organism. For a biodiesel production
platform, for instance, a candidate must be fully adapted to the local climate and operational conditions
to reach the desired lipid productivities (Rasoul-Amini et al., 2014). Efforts regarding the search for this
candidate have been put in practice from as early as the 60s, where organisms have been screened in
nature and characterized in terms of applicability for biodiesel production (Guillard & Ryther, 1962). But
it has been clearly stated that there is a need for improvement through genetic engineering approaches,
once the current lipid productivities and overall fitness of native organisms are not sufficient for a
successful commercialization (Nrel, 1998).
Numerous genetic approaches were performed in E.coli and other organisms for over 60 years (Rotman,
1956). Though, significant advances of genomics in microalgae were only achieved during the last two
decades, especially after the introduction of new sequencing generation systems, which enabled faster
and cheaper genome sequencing (Junying Liu, Song, & Qiu, 2017). The first genetic approach with
microalgae was done with the green alga Chlamydomonas reinhardtii in the 1980’s and since then it
has received considerable attention (Goldschmidt-Clermont & Rahire, 1986). Presently, many
microalgal strains have its nuclear and plastid genome sequenced and mitochondrial DNA and
transcriptome analysis can be found in several cases (Benedito et al., 2010). Although genetic
engineering technologies have been proved successful in microalgae and some progress regarding the
increase of lipid accumulation, poor gene annotation is still a limiting factor (Odjadjare, Mutanda, &
Olaniran, 2017). Hence, identifying and understanding gene functionality, especially associated with
lipids and chlorophyll (chl) metabolism, are highly relevant for engineering more capable straits.
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Nannochloropsis oceanica is a potential candidate for biodiesel production and omega-3 fatty acids,
such as eicosapentaenoic acid (EPA; (Junying Liu et al., 2017). It can accumulate high amounts of
neutral lipids – up to 60% of the total dry weight; and has a high lipid and biomass productivity – up to
297 and 900 mg L−1 d−1, respectively (Xu & Boeing, 2014). A study elaborated by Ma, Wang, Yu, Yin
and Zhou (2014), that evaluated 9 Nannochloropsis strains in terms of productivity and quality of lipids,
had described N. oceanica IMET1 as the best candidate for biodiesel production. This strain has been
genetically engineered before, its genome has been sequenced and is publicly available (D. Wang et
al., 2014). However, its genes are poorly annotated.
Today, identification of gene functionality starts with the use of bioinformatic tools. Well-characterized
proteins can be found in databases which correlate amino acid sequences and respective protein
structures with the encoded genes, and algorithms have been perfectioned to find splicing events as
well as other features (Hoffmann et al., 2014; Saier, Tran, & Barabote, 2006; L. Shen, Shao, Liu, &
Nestler, 2014). However, conclusive results can only be drawn after laboratory testing. An effective
pipeline to study gene functions in a cell are studying mutants that lack a gene or express an altered
version of it. This can be done by targeting individual genes by homologous recombination, therefore
creating a deletion library, or by random mutagenesis. While the first is ideal for an extensive
comprehension of the organism and can lead to vast knowledge, a long period of research and a high
budget and partnerships are necessary to take it further (Patnaik, 2008). The second is often used to
screen genes associated to desirable phenotypes, being the most common the use of chemical and
radiation mutagens (Labrou, 2009). This can quickly generate a large number of mutants that are then
screened for a particular defect of interest, by applying selective pressure. A variation of this technique
is insertional random mutagenesis, which is based on the insertion of exogenous DNA into the genome
in a random manner. When it is integrated into a gene or its regulatory sequences its function is impaired
(Lodish et al., 2000). The main advantages of this method are relative to the molecular tags that serves
as a better strategy for mutant selection, screening and identification of disrupted genes.
In the case of screening for relevant phenotypes, Fluorescence Activated Cell Sorting (FACS) is a
reliable and high throughput technology that efficiently screens and separates cells based on their
complexity, size and fluorescence (Bonner et al., 1972). Subsequently, it has the potential to be an
alternative for screening microalgae mutants based on fluorescence properties. Additionally, it can also
be used for detecting altered lipid and chlorophyll content, by using fluorescent markers such as Boron-
dipyrromethene (BODIPY) for staining neutral lipids and using the natural fluorescence emitted by
chlorophyll (Pereira et al., 2018).
This classical genetic approach – identifying the genes responsible for mutant phenotypes – is most
easily performed in organisms that reproduce rapidly and are amenable to genetic manipulation.
Although microalgae have higher areal productivity than higher plants, its doubling times of 1-day are
very slow compared to bacteria (Lin, 2005). This hinders mutant screening, which may take up to 3
weeks on agar plates, when antibiotic resistance is the selective marker. Therefore, a faster and more
efficient selection approach is necessary to generate knockout mutant libraries. Another limitation of
creating a microalgae mutant library are the transformation procedures. These have been largely
13
established in bacteria and several approaches exist, such as chemical treatment, heat shock,
electroporation, sonication or conjugation (Hanahan et al., 1991). However, algae are more difficult to
transform, especially due to their complex cell wall and low efficiencies of gene expression (Kilian et al.,
2011). For IMET1, electroporation is the most successful applied technique for nuclear transformation
but there is not a large consensus in literature. Therefore, optimization of the parameters that influence
electroporation is necessary, especially for generating mutant libraries in a cost-efficient manner.
1.2. Aim
The aim of this study is to develop an efficient pipeline for producing, selecting and screening
Nannochloropsis oceanica IMET1 random insertional libraries for altered phenotypes of industrial
applications.
- For production of mutant libraries, the electroporation transformation protocol was optimized for
several parameters, such as cell concentration, quantity of DNA carrier and DNA template,
temperature of the recovered media and the use of a cell membrane permeabilizer.
- For mutant selection, a novel approach based on fluorescence markers and cell isolation was
attempted using FACS.
- For screening experiments, a mutant library was analysed and sorted using FACS for altered
phenotypes of chlorophyll and neutral lipid production, with both analyses in a high-throughput
manner.
14
2. Theoretical Background
2.1. Microalgae and its applications in biotechnology
Microalgae or microphytes are photosynthetic microorganisms which vary in size from 2 to 200
micrometers, are polyphyletic and can be found in a variety of saline and freshwater streams on our
planet (Robert A. Andersen, 2006). The evolutionary history and taxonomy of microalgae is complex
and is constantly subjected to revisions due to new genetic and ultrastructural evidences (R. A.
Andersen, 1992). Most of these organisms are classified in the division Chlorophyta, but can also be in
diatoms (Bacillariophyta), Rhodophyta, among others (Bahadar & Bilal Khan, 2013). It is estimated that
exist about 200,000 to 800,000 algae species and so far, in 2004, 50,000 have been described
(Richmond, 2004). A diverse range of these organisms are photoautotrophs, but some may grow under
mixotrophic or heterotrophic conditions. The first only require inorganic compounds like CO2, salts and
light as an energy source, while the heterotrophic are non-photosynthetic, therefore requiring an external
source of organic compounds as an energy source (Brennan & Owende, 2010).
Microalgae have gathered much attention by the scientific community and industry in the last decades
due to their large biotechnological potential for producing value added products for feed, food,
nutraceutical, pharmaceutical industries and biofuels. In addition, these microorganisms can be used in
environmental engineering such as for wastewater treatment, and for bio mitigation of CO2 in flue gases
from coal-fired power stations (Zhou et al., 2015).
The consumption of polyunsaturated fatty acids (PUFAs) is necessary for human nutrition. These
molecules can only be found in marine life due to the primary producers – algae. Although a fish diet
can provide these molecules, enough consumption is seldom achieved for generating health benefits.
And scarcity of fish aquatic reserves could contribute to a decrease in consumption. Microalgae stands
also as an opportunity in the production of algal pigments, such as carotenoids, like beta-carotene,
lutein, lycopene, astaxanthin and zeaxanthin, or less usual pigments such as phycobiliproteins (Cadoret,
Garnier, & Saint-Jean, 2012). In addition to these applications comes the use of microalgae as
expression vectors, where possibilities for diverse applications come in use, for instance for production
of vitamins, antibodies, terpenes and other bioactive molecules of industrial relevance (Pulz & Gross,
2004).
In the case of biofuels, it is possible to produce bioethanol, biobutanol, biodiesel, biohydrogen, and
biomethane by biological or chemical processes (Gonzalez-Fernandez & Munoz, 2017). They can
produce bioethanol by fermentation and there is potential of using this alternative since most biomass
feedstocks which generate bioethanol, such as corn and sugar cane, have a high value for food
applications and require large quantities of land to be produced (Harun et al., 2010). Microalgae can
produce biomethane and contain almost no lignin and lower cellulose, therefore showing a good process
stability and high conversion efficiencies for anaerobic digestion (Vergara-Fernández et al., 2008).
Another biofuel that can be produced from these organisms is biohydrogen, either through green algae
that produce hydrogen under anoxia conditions or as feedstock rich in carbohydrates for fermentative
15
hydrogen production by anaerobic bacteria (Nagarajan, Lee, Kondo, & Chang, 2017). At last, the
capacity of some microalgae to accumulate large quantities of lipids under nutrient-limiting conditions
and their high photosynthetic rates has led to major research efforts into maximizing lipid production for
biodiesel applications (Bahadar & Bilal Khan, 2013; Gonzalez-Fernandez & Munoz, 2017).
2.2. Photosynthesis and lipid metabolism
Aquatic microalgae are considered the most suitable organisms for CO2 fixation. Their features include
fast proliferation rates, a wide variety of tolerance to extreme environments and a photosynthesis
efficiency superior to C plants (Kurano et al., 1995). These unicellular organisms have been used to
study photosynthesis from a long time ago. The work of Melvin Calvin reported in “The Path of Carbon
in Photosynthesis” in 1961 (a Nobel prize lecture), was performed with the use of Chlorella as a model
photosynthetic organism (Calvin & Massini, 1952).
Photosynthesis can be described in 3 steps. The first is the absorption of photons by pigments
embedded in the internal membrane of chloroplasts. These pigments, composed mainly by chlorophylls,
are responsible for transferring electrons to the reaction centres of the photosystems and can absorb a
high amount of energy, from wavelengths of 400 to 700 nm (Trissl, 1993). On the second step molecules
of water are split into protons (H+) and oxygen (O2) due to the transfer of electrons to photosystem II,
therefore being processed into the electron transport chain. The electrons are then transferred from a
mobile carrier (plastacyonin) to the photosystem I, where ferrodoxin is responsible to transfer the
electrons and form NADPH that later leads to the production of adenosine triphosphate (ATP). This
process is often called the “light reactions” of photosynthesis, once the process is triggered with the use
of light. The third step or “dark phase” involves the use of NADPH and ATP in the carbon-reduction cycle
– Calvin cycle, in which CO2 is incorporated by a reaction catalysed by ribulose-1,5-bisphosphate
carboxylase oxygenase (RUBISCO) and converted to form C3 carbohydrate molecules (Hagemann &
Bauwe, 2016; Lambers et al., 2008). A comprehensive scheme, visualized in Figure 1, explains the
process of photosynthesis with both dark and light phases and lipid synthesis.
16
Figure 1. Schematic representation of photosynthesis and lipid synthesis. Energy is passed through the reaction
centres (PSII and PSI), therefore reaching the dark phase, where RUBISCO fixes carbon dioxide to form
carbohydrates in Calvin Cycle. These C3 molecules are then used to synthetize lipids, preferably triacylglycerides
(TAGs) that may be used to produce biodiesel. Yellow arrows symbolize the reactions that could be maximized,
while the losses in purple arrows should be minimized. Retrieved from Stephenson et al. (2011).
Once microalgae cells produce carbohydrates through the photosynthetic apparatus, a fraction is
converted to lipids. Progress in understanding lipid metabolism and the important regulatory
transcription factors has been observed throughout the years for higher plants, while for microalgae it is
still poorly defined. So far, the only microalgae described pathway for triacyglycerides that differ from
higher plants is found for Chlamydomonas reinhardtii (Fan et al., 2011; Mussgnug et al., 2007).
Furthermore, phylogenomic data provided insights into the molecular evolution of lipid biosynthetic
pathway in microalgae and confirm its close evolutionary proximity between lineages of Streptophyte
and Chlorophyte divisions, where higher plants are also included (Misra et al., 2012).
Although lipid composition of algae and its distribution varies according to the species, a consensus is
achieved about its primary destination and synthesis pathway. The lipid production is essentially
performed for the synthesis of plasma membrane and other endomembranes of organelles as well as
for storing energy during the day. Once the cell achieves the necessary lipids for maintenance, de novo
synthesis and accumulation in lipid bodies appear as free fatty acids, produced in the plastids and in the
form of TAGs, assembled in the Endoplasmic Reticulum (ER; Lodish et al., 2000).
Lipid accumulation is influenced by several cultivation factors and in consequence, improving
photosynthesis in microalgae for higher lipid productivities is highly complex (Sforza, Simionato,
Giacometti, Bertucco, & Morosinotto, 2012). One of the most important parameters is light, as it affects
all the metabolism of the cell, especially growth and lipid accumulation (Coppack, 2013). In the case of
Scenedesmus sp. lipid and TAG content increased from 26 to 41% and 16 to 32%, respectively, with
increase in light intensity from 50 to 250 μmol m−2 s−1 (J Liu, Yuan, Hu, & Li, 2012). As for the case of
nutrients in the media, nitrogen is the most critical on the effects of lipid and carbohydrate accumulation
(Illman, Scragg, & Shales, 2000). A lipid content of 48% was achieved in the microalga Nannochloropsis
oculata when the organism was cultured under nitrogen depletion conditions for 4 days. Similarly was
17
performed and verified in Nannochloropsis sp.F&M-M24 (Bondioli et al., 2012). Nitrogen starvation is
the common used methodology to enhance lipid content but is also reported to decrease biomass
productivity, which in some cases may decrease lipid productivity (Singh et al., 2016). Another factor
that affects lipid productivity is temperature. In Converti et al., (2009) lipid content of N. oculata,
decreased from 15 to 8% with increase in temperature from 15 to 20 ºC as the growth rate increased.
Salinity and pH are also important. In most of the cases, higher salinities increase the lipid content and
reduce growth rate (Ho et al., 2014). A study with Chlorococcum sp. reported an increased lipid content
from 10 to 30% and a 4-fold decrease of biomass concentration with an increase in NaCl concentration
(Harwati et al., 2012). As for the case of pH, although greater lipid productivities are normally achieved
with higher pH’s it is more strain specific (Gardner et al., 2011; Santos et al., 2012).
2.3. Molecular tools for microalgae
Significant advances in the field of genomics using microalgae were only achieved during the last two
decades. Especially, after the introduction of new sequencing generation systems, which enabled faster
and cheaper methodologies (Junying Liu et al., 2017). Presently, the majority of studied strains have its
nuclear and plastid genomes sequenced and mitochondrial DNA and transcriptome analysis can be
found in several cases (Benedito et al., 2010). In regard of targeted genetic engineering, research aimed
to increase the accumulation and production rate of lipids in photosynthetic organisms have been
globally performed. Improvements have been accomplished in the modification of the photosynthetic
apparatus and of lipid synthesis and catabolism or even by adjustment of the central cellular metabolism
(Odjadjare et al., 2017). In photosynthesis efforts have been applied to reduce the antenna size, by
modifying chlorophyll synthesis (Ort, Zhu, & Melis, 2011) or by increasing the number of photosystems
with the overexpression of certain genes (Mussgnug et al., 2007); it was also observed that over-
expression of some photorespiratory enzymes, speeding up flux through the photorespiratory cycle,
improves photosynthesis and cell growth (Hagemann & Bauwe, 2016).
However, successful advances using targeted genetic engineering demand a good understanding of
genes functionality. And for this, advances in gene annotation are required, which is still one of the major
challenges in modern biology. Classic gene annotation techniques comprise the creation of mutant
collections, either by targeting genes of interest or by applying random mutagenesis. Both collections
can be used to screen phenotypes that occur under a diverse range of physiological conditions, such
as in nutrient starvation, in saline conditions, in treatment with stress agents like hydrogen peroxide, as
others (Giaever et al., 2002; Junying Liu et al., 2017). The traditional method for random mutagenesis
makes use of chemical agents or radiation for inducing mutations in the genome, followed applying
selective pressure for a certain desirable phenotype. However, for gene annotation this method is not
ideal since to trace back mutations, full sequencing approaches are necessary. Random insertional
mutagenesis is an alternative technique that makes use of DNA insertional cassettes. It relies on
exogenous DNA to be inserted randomly in the genome, producing mutations if the inserted fragment
interrupts a gene or its regulatory sequences (Labarre, Chauvat, & Thuriaux, 1989). The inserted DNA
18
has a known sequence and can serve as a molecular tag that helps consequent gene identification and
cloning (Lodish et al., 2000). For screening gene knockouts, simple phenotypes are easiest to detect,
for example while searching for metabolic deficiency of a specific amino acid or nutrient (Giaever et al.,
2002). However, for more complex phenotypes, more elaborated screens are necessary. With
introduction of new sequencing technologies and the development of complex array technologies, high
throughput systems are now possible for annotating genes. Examples of these technologies are RNA-
seq, microarrays or RNA interference. These could be applied to study the transcriptome, a field that
has opened the way to better understand the genome dynamics. In the case of algae it has been used
in metabolic pathways associated to chlorophyll production as well as responses to stress (Lv et al.,
2013).
Nannochloropsis is a group of great relevance for industrial applications due to their fast lipid production,
especially N. oceanica IMET1 (Meng et al., 2015). Its genome was only sequenced in 2014 – it has 22
chromosomes and 9754, 126 and 35 protein coding genes were predicted for nuclear, chloroplast and
mitochondrial genomes, respectively (D. Wang et al., 2014). It has a total nuclear genome of 31.36
mega bases (Mb) in size and a gene density of 311 genes/Mb. Its genome is much smaller and compact
than Chlamydomonas reinhardtii (121 Mb) and has very high coding potential (52.1%). IMET1 has also
a higher quantity of genes associated with lipid and glycerolipid metabolism when comparing to other
N. oceanica strains – Figure 2. And when comparing to C. reinhardtii, N. oceanica with one fourth of the
genome size has a prominent expansion in gene copy related to the de novo synthesis of fatty acids
and TAGs (D. Wang et al., 2014).
19
Figure 2. Variation in the genome of different N. oceanica strains in terms of gene functionality. Adapted from D.
Wang et al. (2014).
Despite recent advances in genomic studies in N. oceanica, more insights in regard of the gene
functionality, especially for lipid production and photosynthesis are crucial for strain development.
Recent advances in genetic studies with microalgae were only achieved due to the development of
efficient transformation techniques that arose in the last decades. From these, the most prominent are
electroporation, particle bombardment, Agrobacterium tumefaciens - mediated transformation, glass
beads method and the use of cell wall deficient strains (Potvin & Zhang, 2010).The first makes use of
high intensity electrical field pulses where the cell membrane can be temporarily destabilized. During
this period, the membrane is highly permeable to exogenous molecules present in the media, such as
DNA (Donald et al., 2012). It has the advantages of being relatively non-invasive and using no chemical
methods, which doesn’t alter the biological structure or function the cells. The main parameters that
affect electroporation are pulse length, field strength, medium composition, temperature, cell wall and
membrane characteristics and DNA and cell concentrations (Brown, Sprecher, & Keller, 1991). It has
been proved to work in microalgae genera and species as diverse as Chlorella (Maruyama et al., 1994),
20
Dunaliella salina (Sun et al., 2005), C. reinhardtii (Shimogawara et al., 1998), and Nannochloropsis
(Kilian et al., 2011).
Transformation methods are only effective with the use of the proper selection markers. The majority of
these confer resistance to antibiotics or are associated to selection based on cultivation conditions. In a
review made by Griesbeck et al., (2006), several selection markers are listed for Chlamydomonas, and
the majority compatible for other microalgal species. From these are listed a nitrate reductase which
select cells that grow on nitrate as the sole nitrogen source. Or in the same category the use of phosphite
as a sole source for phosphorus, therefore opening the way to less contaminated cultures (Sandoval-
Vargas et al., 2018). Also, the use of antibiotics has been widely reported for microalgae, especially the
antibiotic zeocin (Stevens et al., 1996) or hygromycin (Berthold et al., 2002). Although the use of
antibiotics is largely effective and applied, the generation of false-positives can sometimes be a problem.
These are wild-type cells that resisted the antibiotic selective pressure, either due to physical
degradation (e.g. light) or due to the presence of selection marker proteins that are expressed and
exported by the mutant cells. On the latter are usually observed smaller colonies surrounding the mutant
ones, named satellite colonies.
2.4. Microalgal culturing techniques
The combination of environmental conditions such as light intensity, temperature, photoperiod and
nutrient composition greatly affect microalgae growth conditions (Lin, 2005). In fact, for photoautotrophic
algae, light intensity and photoperiod (light and dark) cycles are the prime factors that determine growth
(Parmar et al., 2011). These use light as the source of energy for synthetizing cell components and can
have light saturation of the photosynthetic apparatus as high as 2500 µmol photon m−2 s−1, in the case
of D. salina (Melis et al, 1998). When microalgae are exposed to light above the saturation point it
cannot use the additional limit (Wahidin et al., 2013). And this is the primary reason why the highest
observed photosynthetic efficiencies are 30% or more lower than theoretical efficiencies (Ort et al.,
2011). Furthermore, this point can change among the depth, density or growth state of the culture. If
cells are cultured at a higher depth or higher concentrations, the light intensity must increase to pass
through the culture, due to cell to cell shading behaviours. Therefore, optimization of the culturing
conditions must always be carried out to maximize the photosynthetic efficiencies. A parameter that is
related to the amount of light that is reached in cultures is the photoperiod. For Nannochloropsis sp. the
best parameters for lipid accumulation involved a photoperiod of 18:06 light: dark cycle and a light
intensity 100 µmol photon m−2 s−1 (Wahidin et al., 2013).
Microalgae can be divided into freshwater or marine species, while some can tolerate both salinities or
prefer brackish waters (Gonzalez-Fernandez & Munoz, 2017). Also, they are so diverse that some can
be cultivated mixotrophically or heterotrophically, as it is the case for some species of Chlorella (Gao et
al., 2010; Miao & Wu, 2004), Scenedesmus obliquus (Shen et al., 2015) , Tetraselmis suecica (Zittelli
et al., 2006), Neochloris oleoabundans (Morales-Sánchez et al., 2014) or even Nannochloropsis sp.
21
(Fang et al., 2004). Microalgae are organisms that grow in several different media and can also tolerate
high quantities of heavy metals (Suresh et al., 2015). Many media have been proposed for their
cultivation but the main chemical elements that green algae require are nitrogen, phosphorus, calcium,
magnesium, sulphur, iron, copper, manganese and zinc (Borowitzka & Borowitzka, 1988). Apart from
media, a parameter that also influences growth is the CO2 supply. It is indispensable for autotrophic
growth and is of great interest for polluting companies, as it serves as a carbon sink for lowering
greenhouse gas emissions. Carbon dioxide can greatly affect cellular growth and some algae can
tolerate high concentrations (Jiang et al., 2011). Chlorella and Scenedesmus can have growth
unaffected by CO2 concentrations in the range of 10-30% (Hanagata et al., 1992). Also, when supplying
it for Scenedesmus at 100% dissolved CO2, although growth was inhibited, reversing it is still possible
when lowering to 20% concentration (Hanagata et al., 1992). Temperature is a crucial factor for cellular
growth and greatly affects lipid productivity. In Converti et al., (2009) lipid content of N.oculata,
decreased from 15 to 8% with temperature rising from 15 to 20 ºC and also as the growth rate increased.
Salinity and pH are also two important conditions for growth but also for lipid accumulation. In most
cases, higher salinities increase the lipid content and reduce growth rate (Ho et al., 2014). A study with
Chlorococcum sp. reported an increased lipid content from 10 to 30% and a 4-fold decrease of biomass
concentration with an increase in NaCl concentration (Harwati et al., 2012). As for the case of pH, though
superior lipid productivities are normally achieved in alkaline pH, it is also very strain specific (Gardner
et al., 2011; Santos et al., 2012).
Cultivation of microalgae can be performed either in liquid or solid media. Also, they can be cultivated
in 24, 48 or 96 well-microplates, which allows high through-put screening approaches. As advantages,
microplates are fast to operate, do not require intensive labour and are low cost. They have been used
as a tool for kinetic studies, can be used for toxicity tests (Blaise & Vasseur, 2005) and even results can
be used to interpret large scales of cultivation (Van Wagenen et al., 2014). With this comes the ability
to perform screening experiments without the cost and labour of using photobioreactors.
2.5. Fluorescence Activated Cell Sorting
Mutant screening with antibiotic resistances is performed in a wide area of research and it is also
reproducible in microalgae. However, algae doubling time is relatively high, limiting its use as an efficient
screening methodology. Therefore, a faster mutant screening procedure is necessary. In this sense,
Fluorescence Activated Cell Sorting is an automated and very effective technology that greatly
enhances the isolation and screening in microbiology, in this case, for microalgae. It uses Flow
Cytometer (FCM), an equipment built in the 1960s as a way of counting and analysing optical properties
of single cells suspended in a fluid (Robert A. Andersen, 2006). In fact, with FCM, single microalgal cells
can be counted, examined or sorted according to their features or physiological state, like metabolic
activity, viability, composition or morphology (Hyka et al., 2013). The term FACS is applied when FCM
is equipped with a sorting module allowing the separation of cells based on fluorescence either from
photosynthetic pigments (auto-fluorescence) or from applied fluorescent probes in the sample to be
22
analyzed (Hyka et al., 2013). It allows the process to be taken one very important step further, once it
instantly analyses and separates cells with desirable characteristics (Ghosh et al., 2016).
Droplet sorting is the most common mechanism in the FACS. It is based in a stream-in-air configuration
in which a liquid cells sample is converted into droplets containing single cells, by using mechanical
vibration of the nozzle tip (Yang et al., 2006). This methodology is described in Figure 3. Typically, the
fluid flow stream leaves the flow cell through an orifice of defined diameter. The laser intercepts the cells
at the interrogation point, where measurements are made. If a cell meets the criteria of the sort logic,
the flow stream is charged (positive or negative) just before the break-off point of the droplet that
contains the target cell. The droplet retains the charge and is deflected by the charge plates toward the
collection tube or plate (Robert A. Andersen, 2006; Hyka et al., 2013).
Figure 3. Schematic diagram concerning the working mechanism of droplet cell sorting in the FACS (a). Image of
a typical flow stream at the droplet break-off point (b). Adapted from Robert A. Andersen, (2006).
The most limiting disadvantages of FCM applications seem to be the necessary standardization of
methods and measurements, once calibration, control and validation are required to provide consistent
data. Comparison information from various laboratories using different FCM devices is generally
complex and occasionally not possible. Also, it requires sophisticated data analysis, instrument prices
are high and demands good operator skills (Müller & Nebe-Von-Caron, 2010; Wang et al., 2010).
23
Overall applications of FCM include the control of biomass composition and cell viability, identification
and counteracting of stress factors, selection of cells with targeted features, and isolation of axenic
cultures (Hyka et al., 2013). Once events are gated on plots, targeted cells can be sorted for further
analysis. Commercially available flow cytometers can sort up to 1000 – 100.000 cells per second and
have a variety of sorting modules, such as collection tubes, microplate wells or agar plates, depending
on the equipment brand. The flow tip orifice diameter dictates the droplet diameter that are created in
the nozzle (Bonner et al., 1972). These can have different sizes: 50 µm which can be used to sort
chromosomes, bacteria or organelles, 70 µm to sort lymphocytes and other small cells, 100 µm to sort
cultured cells and 200 – 400 µm for larger particles, such as embryos Herzenberg et al., 1979; Horan &
Wheeless, 1977). Furthermore, other parameters that are influenced by the nozzle sizes are the sorting
speed and cell viability. The smaller the size, the higher the pressure that is required to generate a stable
droplet breakoff (Elliott, 2009). Hence, more fragile cells may require lower pressures and therefore
larger nozzles.
When a cell is detected during sorting, it is difficult to discriminate between a passage of a large cell or
small multiple cells that are adherent to each other. To overcome this, the sorting modes consider these
event coincidences by determining the rules used for the sorting decision. In Figure 4, the differences
in sorting applied in the equipment SH800 (Sony Corporation®) are enumerated.
24
Figure 4. Sorting modes of the SH800 Cell sorter (Sony Corporation®) for the different sorting chips. Adapted from
Cell Sorter Operator’s Guide, retrieved from https://danstem.ku.dk/images-new-website/LE-
SH800_Operators_Guide_EN_Rev10.pdf.
A high degree of purity of the sorted events requires the targeted cells to be separated from other events,
which mean that droplets containing the targeted events are sorted, while the unwanted ones are passed
to the waste disposal. Sorting can be applied in different modes of purity and yield. When sorting in
purity modes, the number of targeted events rejected will maintain the high purity but reduces the
number of cells that are screened per time (therefore reducing yield). Conversely, when aiming for yield
mode, more targeted events will be sorted at the cost of purity. Therefore, this trade-off needs to be
considered for the type of experiment that needs to be performed. The first is for example ideal for
isolating a pure population and the second for recovering as many events as possible.
The combination of FCM and fluorescent dyes is an important approach to determine certain cell
features. After selection of an appropriate dye (to study the desired cell feature) the staining protocol
should be optimized, so that the permeability of the fluorescent dye is increased (Hyka et al., 2013).
Since some microalgae have a specific cell wall composition and structure, fixation and permeabilization
procedures require increased attention with respect to the staining method (Collier, 2000; Sosik et al.,
2010).
25
Microalgae and other phytoplankton possess fluorescing endogenous pigments such as chlorophylls
and carotenoids. For chlorophyll autofluorescence of algal cells a blue laser (488 nm) is commonly used.
In the case of research with the use of external fluorescence dyes or probes, the fluorescence
wavelength needs to be carefully chosen, since strong pigment autofluorescence can cause interference
or quenching of a dye if it fluoresces within the same spectrum (Müller & Nebe-Von-Caron, 2010).
Therefore, ideal fluorescent dyes/probes chosen for the investigation of microalgae should have: a
maximal fluorescence intensity in the range of 500 – 600 nm; be non-toxic, sensitive at low
concentrations and show excitation at the emission wavelength of the FCM laser (Ghosh et al., 2016;
Hyka et al., 2013).
For screening of microalgae for biodiesel using the FACS, Nile Red (NR) fluorophore is usually applied
to stain the samples, since it targets polar and neutral lipids (W. Chen et al., 2009). An alternative
compound for NR is the lipophilic fluorescent dye BODIPY 505/515. This fluorophore has been shown
to have a narrower emission spectrum than Nile Red, making it more valuable for confocal imaging
(Mutanda et al., 2011). Also, unlike NR it does only stain apolar lipids and doesn’t bind to cytoplasmic
compartments other than lipid bodies and chloroplasts (Mutanda et al., 2011).
3. Materials and Methods
3.1. Strain and growth conditions
Nannochloropsis oceanica IMET1 was kindly provided by Jian Xu from Qingdao Institute of Bioenergy
and Bioprocess Technology. It was cultured in liquid or in solid (1% VWR agarose) media, containing
artificial seawater (ASW; 419.23 NaCl, 22.53 Na2SO4, 5.42 CaCl2·2H2O, 4.88 K2SO4, 48.21 MgCl2
·6H2O, 20.00 HEPES – all values in mM) supplemented with 2 mL L-1 of the commercial kit Nutribloom®
(NB; Necton, S.A). Cultures were inoculated in autoclaved borosilicate Erlenmeyers flasks and cultivated
under controlled conditions with a Multitron Pro incubator (Infors HT) at 25ºC, light intensity 180 µmol
m-2 s-1, 0.2% CO2 supply and light: dark cycle of 18:6 h. Cultures were also grown in 24 and 48 well-
microplates under the same cultivation conditions. A lower light incubator (~ 60 µmol m -2 s-1 and 16:8
h light/dark cycle) with the same temperature but without CO2 supplementation was used to grow cells
on agarose plates and to keep back up liquid cultures. All inoculation procedures and culture handlings
were performed in sterile conditions.
3.2. Optical density and quantum yield measurements
Culture growth was monitored by measuring optical density at 750 nm (OD750) using a
spectrophotometer (Hach Lange DR6000). Polystyrene cuvettes of 1.5 mL were used to pour 1 mL of
sample. When OD exceeded values greater than 0.8, NB medium was used for diluting the sample.
Quantum yield (QY) of photosystem II (PSII) is given by the ratio of photons that are used in
photosynthesis to the total absorbed photons, therefore accounting the photosynthetic performance.
26
This number can be used to routinely check the culture state. QY measurements were done with an
AquaPen - AP100 (Photon System Instruments), a fluorometer equipped with Fluorpen software
(v1.2.1.1).
3.3. Genomic constructs and transformation of N. oceanica IMET1
3.3.1. Plasmids design
For this study, 4 plasmids carrying different selection markers were designed (Table 1). Fluorescent
proteins (FPs) kusabira orange (mKO2), mCherry and green fluorescent protein (GFP) and antibiotic
zeocin resistance Sh ble gene were the selective markers for the different plasmids. For pNIM16,
pNIM17 and pNIM18, FPs were used in combination with Sh ble. In both scenarios, a cleaving
sequencing peptide (P2A) was put in between the two selection markers, so that the disruption of
peptide bond formation could result into two separate translated proteins. P2A was used according to
(Poliner et al., 2018).
Table 1. General characteristics of the designed plasmids. Name of plasmids and insertional cassettes, selection
markers, additional features and length of the constructs.
Plasmids & Insertional
cassettes (IC) Selective markers Additional features
Length of
plasmid / IC (bp)
pNIM16 (IC16) mKO2 + Sh ble
P2A
5612 / 2923
pNIM17 (IC17) mCherry + Sh ble 5666 / 2971
pNIM18 (IC18) GFP + Sh ble 5672 / 2977
pNIM21 (IC21) Sh ble
Random sequence 3’ after
terminator and larger VCP
promoter
5029 / 3682
The designed plasmids have in common the backbone from the vector PUC19 (New England BioLabs®),
that carries a bacterial ampicillin resistance cassette and an origin of replication for E.coli TOP10. Every
IC used for mutagenesis experiments carried the endogenous promoter of violaxanthin chlorophyll a
binding protein precursor (VCP) and its first intron. As terminators the endogenous α-tubulin terminator
region (TATUB) and the ATP-dependent Clp protease proteolytic subunit terminator region (CLPP) were
used. An elementary structure, common for both plasmids, can be viewed in Figure 5.
Figure 5. General structure of the 4 plasmids. Backbone comprises the ampicillin resistant gene and E. coli origin
of replication, used for E. coli cloning steps. Insertional cassette of all plasmids contains the VCP promoter and
intron splicer, differ at the selection markers and have the same terminators (alpha tubulin - TATUB, and CLP
protease (CLPP)). Additional features such as the P2A sequence and a random 3 prime sequence was not
represented. Furthermore, the fragment sizes here represented do not correspond to the real scale in terms of base
pair length.
27
The software Snapgene® (version 4.1.9) was used for plasmids design and other insilico approaches,
such as sequencing alignments and visualization of primers and restriction enzyme cuts. Primers were
designed using Primer3 webtool (http://primer3.ut.ee/) and the suitability of its PCR parameters was
confirmed by PCR primer stats (http://bioinformatics.org/sms2/pcr_primer_stats ). Primers were ordered
from Integrated DNA Technologies®. All selective markers were codon harmonized for IMET1 and
ordered as gBlocks® (Integrated DNA Technologies) with the exception of mCherry, which was donated
by a colleague that previously codon optimized it for Acutodesmus obliquus.
i) Amplification and purification of genomic constructs
The backbone together with the promoter, terminator and VCP intron (that together form a construct for
assembly) and the Sh ble zeocin resistance gene were amplified by PCR using a previous digested
plasmid as a template. The FP mCherry was also amplified from a digested plasmid. A preparative PCR
with a total reaction mixture of 50 µL was performed for all the 3 genomic constructs using Q5® High-
fidelity DNA Polymerase. DNA was then purified with the Zymo® purification kit according to the standard
protocol. DNA concentration and purity were read with NanoDrop One (ThermoScientific). Purification
results and PCR settings in Supplementary File 2.
ii) Plasmids assembly and miniprep
The genomic constructs assembly was performed with Gibson Assembly, by following the NEBuilder®
HiFi DNA Assembly Reaction Protocol (New England BiolabsTM). And supported by the accompanying
NEBuilder™ online assembly tool for primer design. To do this, overlap sequences of 25 base pairs (bp)
of the adjacent fragments were designed in the fragment amplification primer sets and from the gBlocks
ordered sequences. All reactions followed similar molarity ratios between the ligated fragments. A 10
µL reaction was used with 2-fold NEBuilder reaction mix and with 5 µL of DNA template and nuclease-
free water. Reactions were run at 50ºC for 1 hour. After this, 5 µL of the total reaction volume was used
to transform E.coli TOP10. Mix & Go E.coli Transformation Kit & Buffer Set (Zymo ResearchTM) was
used to make strains competent for up taking DNA. Addition of assembled DNA to competent cells
mixture was carried out in ice, followed by an incubation time of 5 min. After this, 10 µL and ~ 95 µL
from the incubate mixture were separately plated in pre-warmed agar plates at 37ºC, containing LB
medium and ampicillin at 100 µg mL-1 (standard concentration for every experimental use in this study).
Plates were grown overnight at 37ºC.
E.coli colonies were identified and screened with colony PCR, with primers targeting each plasmid-
insertional cassettes. One or two colonies of each plasmid, with an expected band size were inoculated
in LB liquid medium containing ampicillin and incubated overnight for plasmid preparation (37ºC and
250 rpm) using a Climo-shaker ISF1-X (Kuhner). Plasmid DNA was purified using the kit GeneJet
Plasmid Miniprep (ThermoScientificTM) according to the indicated protocol and DNA concentrations were
quantified. Restriction enzymes were used to confirm correct plasmid assemblies by generating
expected restriction maps. Digested colonies with positive results were then sent for sequencing for
further validation.
28
3.3.2. DNA preparation for transformation
Purified plasmid DNA from all the 5 constructs was linearized with SchI (ThermoScientificTM). The
digestion was then purified and used as a template for preparative PCRs. These were performed with
plasmid-specific primer sets targeting the ends of the insertional cassettes. All amplifications were
followed in a reaction mixture of 50 µL using Q5 polymerase and 0.5 uM primer concentration. PCR
products were purified using the DNA Cleanup Microkit #K0832 (ThermoScientificTM), according to
manual, with exception for step 4, where the flow-through was reloaded in the column and centrifuged
at the same conditions. PCR and purification steps were optimized for every insertional cassette and
can be accessed in Supplementary File 2.
3.3.3. Transformation of N. oceanica IMET1 for generating knockout libraries
Transformation of N. oceanica was performed with fresh exponential growing cultures with a maximum
OD750 of 1.0 and a minimum QY of 0.70. Cells were transferred to 50 mL falcon tubes and harvested at
2500 G for 5 min using an Allegra X-30R Centrifuge (Beckman Coulter). Medium was discarded, and
the pellet was washed 3 times with 12.5 mL of sorbitol 375mM (Li et al., 2016). Cell concentration was
then adjusted to 5E9 cells mL-1, based in a 20% of cell losses, due to washing and centrifugation. All
steps were performed at 4ºC.
Electroporation cuvettes of 2mM (Pulsestar, Westburg©) were used with a 200 µL of total volume,
including 1 µg of template DNA and Salmon Sperm DNA (SSD; Sigma-Aldrich) in 40-fold excess over
DNA template, mixed with washed cells. DNA was firstly added to each electroporation cuvette, followed
by the cell and SSD mix. For electroporation a gene pulser Xcell® (Biorad) was used with a decay
exponential protocol and 11 kV cm-1, 600 uΩ shunt resistance and 50 μF capacitance as standard
settings (Li et al., 2016). Right after the pulse, 1 mL of ASW with NutriBloom® (further mentioned NB
media) was quickly added to the cuvette and mixed thoroughly with the pipette. Cells were transferred
to falcon tubes that were kept overnight in a low light chamber for associated stress recovery. Mixed
SSD and cells, electroporation cuvettes, NB media and template DNA were kept in ice during the entire
protocol. The day after, cells were harvested at standard conditions (2500 G; 5 min; 25ºC), supernatant
was discarded, and the pellet resuspended in the remaining medium (~ 200 µL). Cells were then plated
on agarose plates with NB medium containing zeocin (Invitrogen®) for transformants selection. A
concentration of 3 µg mL-1 of zeocin was the standard for every NB liquid or solid media with this
antibiotic.
3.4. Fluorescence Activated Cell Sorting
N. oceanica IMET1 cells were analysed and sorted using a Cell Sorter SH800S (Sony Corporation®)
equipped with a 488 nm laser and sorting chips of 70 µm and 100 µm nozzle sizes. Cell fluorescence
was detected in 2 scatters (forward scatter and back scatter – FSC and BSC, respectively) and in 6
different fluorescence channels (FL1 – FL6). For neutral lipids staining was used the dye BODIPY™
505/515 (4,4-Difluoro-1,3,5,7-Tetramethyl-4-Bora-3a,4a-Diaza-s-Indacene; InvitrogenTM), (Govender et
29
al., 2012). BODIPY stocks of 100 µg mL-1 dissolved in Dimethyl sulfoxide (DMSO) were used for staining
with a previous optimized procedure. During the staining time, samples were kept in the dark due to the
light sensitivity of the dye.
Flow cytometry workflow was based in a gating hierarchy so that analyses of microalgal cells without
doublets and triplets formation are possible. Forward scatter (FSC) and back scatter (BSC) data were
firstly used to create a “All events” plot, where events are visualized for size and complexity, respectively
(Bonner et al., 1972). Events with larger size and complexity were gated (“Cells”) and plotted using FSC
area (FSC-A) and FSC height (FSC-H) to get rid of doublets and triplets. The same was performed using
backscatter data by creating the gate BSC singlets. This last gate was then used for collecting data on
all fluorescence channels (FL1- A to FL6-A), visualized in logarithmic scale.
Flow cytometry data, collected in the 6 fluorescence channels was used to interpret differences in
chlorophyll, lipids and mutants expressing fluorescent proteins. The different fluorescence channels and
respective data interpretation can be accessed in Table 2.
Figure 6. Gate hierarchy used in the Sony cell sorter software. Forward and Back Scatter detectors are first used to plot all events. Standard gates: Cells, FSC singlets, BSC singlets. The latter is used for visualizing fluorescence data on fluorescence channels (FL1 – FL6). All event plot and FL1 to FL6 plots are plotted in logarithmic scale.
30
Table 2. Flow cytometer fluorescence channels and its biological interpretation in this study.
FL1 Green fluorescence (525 nm ±50) GFP + BODIPY
FL2 Yellow Fluorescence (585 nm ±30) mKO2
FL3 Yellow- Orange Fluorescence (617 nm ±30) mKO2 + mCherry
FL4 Orange-Red Fluorescence (667 nm ±30) mCherry and Chlorophyll a
FL5 Red Fluorescence (720 nm ±60) Chlorophyll a
FL6 Far Red fluorescence (785 nm ±60) Chlorophyll a
Cell sorting was performed in different sorting modules - 15 mL falcon tubes; 48, 96 and 384 wells
microplates and agarose plates, all containing NB media. Normal and semi-yield sorting modes were
used for sorting high event numbers, as single-cell and ultra-purity modes for lower counts or for cell
isolation.
Flow cytometry data was collected using the Cell Sorter Software from Sony Corporation (version 2.1.3).
All data was exported into .FCS files and read by Rstudio® (version 1.1.423), based on the R Project
through a dedicated script, built with Bioconductor© functions complemented with the package Flowcore,
the latter specifically for analysing Flow Cytometry datasets (Hahne et al., 2009).
3.5. Method Development Experiments
3.5.1. Optimization of electroporation parameters
An efficient protocol to generate knockout mutant libraries requires an optimized transformation protocol.
The evaluated parameters in this study were the DNA template quantity and cell concentration; the
amount of SSD; the effect of cold NB recovery medium, based on the principle that cells at 4ºC would
take longer to repair membrane damage induced by electroporation, therefore increasing the flux of
DNA delivery (Brown et al., 1991); and the use of Dimethyl sulfoxide once it is commonly used as
membrane permeabilizer. DMSO (Merck KGaA) was applied as a pre-treatment during 15 min, before
pulse application. All tests were performed with at least 2 replicates.
3.5.2. Cell viability after sorting and staining with BODIPY
The cell loss associated with multiple rounds of sorting and pre-staining with BODIPY was evaluated
using a mutant colony isolated from an agarose plate. The isolate was cultured in an Erlenmeyer flask
with NB medium containing 3 µg mL-1 of zeocin during 2 days under high light conditions. One mL
sample was taken during exponential growth and QY > 0.70 for the experiments. Agarose plates
containing NB medium with zeocin 5 µg mL-1 and ampicillin 100 µg mL-1 were used with a 384 well
sorting module of the FACS. Samples were sorted in ultra-purity mode. Tested conditions are presented
in Table 3.
31
Table 3. Sorting and staining conditions used in the cell viability experiment.
Plate ID Condition tested
Plate A 1 round of sorting
Plate B 2 rounds of sorting
Plate C 3 rounds of sorting
Plate D Single BODIPY staining and 1 round of sorting
Plate E Single BODIPY staining and 2 rounds of sorting
Plate F Double BODIPY staining and 2 rounds of sorting
Plate G Control Agarplate A (no antibiotics)
3.6. Screening knockout libraries for altered profiles of lipids and chlorophyll
3.6.1. Mutant library preparation
The knockout library used for screening experiments derived from the optimization trials of the
transformation protocol. Colonies were counted by applying an efficiency ratio to not include false
positives. For this, colonies were discriminated between big, medium or small, and 20 isolates of each
type were re-plated into fresh zeocin agarose plates. After growing on solid media for one-week, false
positives bleached, by losing chlorophyll, while mutants showed visible growth in the plate. An efficiency
ratio for every colony type was calculated and colony forming units (CFU) were visually counted by
applying this ratio.
Mutant colonies grown on agarose plates were flushed with 25 mL of NB media for every 500 pooled
colonies and placed in individual falcon tubes – see Figure 7. From each tube, 100 µL were plated on
agarose plates containing zeocin to make backups. Subsequently, the 500 mutants were pooled in
falcon tube pairs to create 1000 mutant pools, combined by similar ODs (based on empirical evidence).
Optical density was measured for 8 tubes, each containing approximately 1000 mutants and a certain
volume was taken to inoculate level 1 (lvl 1) pools in Erlenmeyer flasks, with a total volume of 50 mL
and an OD of 0.2. A WT liquid culture was also inoculated with the same OD and was used as a control.
Remaining biomass is put in four 250 mL Erlenmeyer flasks with zeocin and placed in the low light
incubator for backups. From each two lvl 1 pools, 25 mL are taken to inoculate level 2 (lvl 2) pools, with
2000 mutants. From every lvl 1 pool, 6.9 mL were taken for creating a level 3 pool, containing 8000
mutants and with a total volume of 55 mL. From this pool 5 mL was used to make a big diluted lvl 3 pool,
further used for screening chlorophyll altered profiles. All lvl 2 pools were put at the high light incubator
with 50 mL volume and an OD750 of 0.2. Screening experiments were carried out with level 2 and level
3 pools after 2 days, to allow cell adaptation in the liquid culturing conditions. All level 1 pools were kept
in the low light as backups.
32
Figure 7. Experimental set-up for preparing a knockout mutant library in level 2 and level 3 pools in liquid media.
Flushing step – from agarose plates to tubes in liquid media containing 1000 mutants; level 1 pools – for creating
backups and level 2 pools; level 2 pools – for creating level 3 pools and both used for screening experiments.
3.6.2. Screening the mutant library for lipids and chlorophyll altered phenotypes
One mL samples were taken from cultures of level 2 and level 3 pools in exponential growth to screen
the highest and lowest cell populations of lipids and chlorophyll fluorescence profiles. In these
fluorescence populations were created 5%, 0.5% and 0.05% gates, by using data collected at the FL1-
A and FL5-A channels. A microplate sorting module was installed in the collection area and ethanol 70%
v/v was sprayed to apply semi-sterile conditions. One hundred events of the respective gates were
sorted in ultra-purity mode into the respective 48 microplate wells. This step is mentioned further as
Day0. Henceforth, all the volume from level 2 and 3 pools were centrifuged (2500 G; 5 min, 25 ºC) and
the pellet was resuspended in NB media without nitrogen (NB – N- media) for inducing starvation and
subsequently lipid accumulation. Pools were screened and sorted in the following 2 days for altered
profiles (mentioned as Day1 and Day2), with the same sorting settings as Day0, with exception for low
chlorophyll gates, where sorting was not applied. Microplate sorting layouts and designed gates can be
accessed in Supplementary File 3 and Results and Discussion - 4.2, respectively. All sorted Day0, Day1
and Day2 microplates were grown in the high light incubator for approximately 2 weeks.
When green was observed in the microplates, altered phenotypes were confirmed and sorted in single
cell mode for mutant isolation. All samples were plotted in histograms using Sony® Cell Sorter software.
Samples were overlapped with at least 5 controls. All the phenotypes that exceeded the fluorescence
of controls or that had one or more skewed or bimodal distributions, suggesting altered phenotype-
populations, were gated and sorted in single cell mode. For this, agarose rectangular plates containing
33
NB medium supplemented with 5 µg mL-1 of zeocin and 100 µg mL-1 of ampicillin were used. Events
were sorted in one more column numbers of a 384-well microplate module of the FACS, according to
plate layouts visualized in Supplementary File 4.
Single isolated mutant cells were grown for 3 weeks in the low light incubator. Colonies were inoculated
in 48 well microplates and grown in the high light for a minimum number of 3 days before analyses for
phenotype confirmation.
3.7. A selection mutant approach using Fluorescence Activated Cell Sorting
3.7.1. Confirmation of IC16 – IC18 mutant phenotypes
After transformation with standard electroporation settings, cells were recovered overnight and plated
on agarose plates. Colonies were inoculated in liquid NB medium with antibiotic and grown in 24 well-
microplates until reaching an OD of approximately 0.5. Phenotypes were confirmed using the FACS by
observing differences in the FL2, FL3 and FL1 (mKO2, mCherry and GFP, respectively) of mutants and
wild-type control with the same applied conditions.
3.7.2. Selecting IC18 mutant knockouts using FACS
N. oceanica WT cultures growing at an exponential state were harvested in a healthy state (QY > 69)
and transformed with 1.5 µg of IC18 (GFP fluorescent marker) with the optimized transformation
protocol. Each of the 10 electroporation samples was recovered with 5 mL of NB media and pooled into
50 mL falcon tubes. Both tubes and controls were incubated overnight in the high light incubator (Infors
HT at 180 µmol m-2 s-1). The day after, mutant samples were centrifuged at standard conditions (2500
G, 5 min, 25ºC) and concentrated 25 times in fresh NB media. Hereafter, 10% of the volume was plated
in an agarose plate containing zeocin. The remaining volume was diluted in NB media and ran in the
FACS for mutant selection. The green fluorescence channel (FL1) was plotted against FSC-A and the
highest green fluorescent cells along the entire FSC-A distribution were gated for the top 2.5% of highest
FL1 fluorescence – see gates in Supplementary File 5. Hence, the gated cells were sorted at semi-yield
mode and at high event speeds (~100 000 events per second) to a 500 mL beaker, already with 250
mL of NB Media. Doublets and triplets with overlapping fluorescence for the FL1 – 2.5% gate were also
sorted to prevent the loss of mutants, resulting in a 30% of total sorted events. Ethylenediamine
tetraacetic acid (EDTA) was added at a concentration of 6 mM to avoid clump cell formation and further
chip clogging. After sorting, cells were centrifuged at standard conditions and resuspended into 3 mL of
NB media. 10% of the volume was used for plating in zeocin agarose plates and the rest was put in the
high light incubator overnight for recovery of sorting and centrifugation stress. The day after, resorting
was performed at lower events per second (EPS), while discarding doublets and triplets and aiming
instead for 5% of the high FL1 population. Samples of 100 µL of cells were taken for analyses after
sorting and the remaining volume was plated on agarose plates containing zeocin. Agarose plates were
grown in the low light chamber until colony formation for further results interpretation.
34
4. Results and Discussion
4.1. Method Development Experiments
4.1.1. Optimization of the transformation protocol
Generating a mutant knockout library through random mutagenesis can be very time consuming and
requires a wide range of costly resources. To counter this problem, the need of an efficient
transformation protocol is necessary for N. oceanica, since literature regarding its optimization is scarce.
Furthermore, Nannochloropsis yields of transformants on DNA are low when compared to other
microalgae species such as C. reinhardtii, where a transformation frequency of 2 x 105 transformants
per µg of DNA is possible using electroporation (Shimogawara et al., 1998). However, in the case of N.
oceanica the frequency can be 80 times lower, with a reported maximum frequency of 2500
transformations per µg of DNA (Kilian et al., 2011).
The initial protocol starts by harvesting exponential IMET1 cultures with centrifugation and washing 3
times with sorbitol. Cells were concentrated to an estimated concentration of 5E9 cells mL-1 and mixed
with SSD at a 40-fold concentration of the template DNA. The latter, at a concentration of 1 µg per
sample. After the pulse application, room temperature NB media was added quickly to the sample.
Samples were kept overnight for stress recovery at low light and plated the day after on agarose plates
containing zeocin as selection marker.
The tested electroporation parameters include the DNA quantity, cell concentration, temperature of NB
media for cell recovery from electroporation, exponential or late exponential growth state of the
transformed cultures, concentration of salmon sperm DNA and DMSO pre-treatment. For accessing the
quantity of DNA used per each electroporation sample, dilutions with pure H2O were prepared and
pipetted into each cuvette before adding cell and SSD mixture. For cell concentration, dilutions were
performed using sorbitol. For testing the temperature of NB media, a falcon tube was kept in ice for the
4ºC condition and for the 25ºC another tube was kept at room temperature. For evaluating the
concentration of SSD different dilutions in pure H2O were applied to the cells before pulse application.
In the case of DMSO, dilutions were prepared and added to the cell suspension every 15 min before the
electroporation pulse.
Insertional cassette 21 with a selection marker for zeocin was used for testing all parameters with an
optimized protocol for DNA amplification and purification – see Supplementary File 2. After growing for
4 weeks, CFU were counted and a false positive ratio was applied. Results can be visualized in Figure
8.
35
Figure 8. Optimization of electroporation parameters. (A) – exponential and late exponential growth stage of
cultures, n=8; (B) – cell concentration in the electroporation cuvettes, n=2; (C) – concentration of DNA per sample,
n=4; (D) – quantity of SSD times DNA template concentration, n=2; (E) – influence of recovery medium
temperature, n=4; (F, G) – DMSO pre-treatment (% v/v – volume of pure DMSO per total volume in the cuvette),
with and without SSD, n= 2 and n=4, respectively. All error bars are given in standard deviation (SD).
36
Exponential and late exponential cultures, Figure 8 – (A), cultivated under the same high light conditions
were grown to different growth stages and harvested at an OD of ~ 0.75 and ~ 1.50, and QY of 0.68 and
0.65, respectively. Results show that the number of CFU between the two groups are significantly
different, with exponential cultures providing higher numbers than late exponential cultures.
Electroporation is a transformation technique that creates damage on the cell membrane and wall (D.
C. Chang & Reese, 1990). In addition, its procedure before the pulse is also harsh, especially for marine
species, where salts must be washed for avoiding overflow of the electrical current. It is possible that
late exponential cultures may not cope so well with electroporation stress, since cellular repair
mechanisms and osmosis regulation could be compromised by a lower immediate nutrient availability
(Beardall et al., 2009).
Cell concentration is expected to affect electroporation efficiencies with its decrease, since there is a
lower number of cells to uptake and integrate DNA in the genome. A maximum yield of 19 ±14 (SD) was
reported for the highest tested group (5E9 cells mL-1) and other groups showed insignificant cell counts
(≤4 CFU for 1E9, 1E8 and 5E7 cells mL-1), Figure 8 – (B). As cells counts were low, results could not be
interpreted. Additional studies, ideally with other optimized parameters, are necessary to achieve higher
cell counts.
To observe potential differences in DNA quantity used per sample, 1 µg and 2 µg of DNA were tested
in 4 replicates. In these tests SSD was not used. Results show that there are no significant differences
between the 2 means, since the standard deviation (SD) in each group was too high, Figure 8 – (C). In
this regard, it might be possible that the quantity of DNA is not a limiting factor for obtaining higher cell
counts.
Salmon sperm DNA is often used in yeast transformation as a DNA carrier and has been reported to
increase transformation efficiencies from 10 to 100 fold (Burgers & Percival, 1987). It is hypothesized
that DNA in bulk increases the chance of the DNA of interest making it to the nucleus without being
degraded by nucleases (Gietz et al., 1995). Five concentrations of SSD were tested – 0, 50, 100, 150
and 200 µg mL-1 (respectively, 0, 10, 20, 30 and 40-fold of the template DNA concentration), Figure 8 –
(D). Results show that the decrease in concentration of SSD increased the number of CFU per plate,
with its negative control (without SSD) having the highest counts – 800 CFUs. Such tendency is not
observed in literature, where SSD increases the number of transformants. A study that optimized
electroporation parameters for Chlamydomonas reinhardtii, has published a steep increase in the
number of transformants by increasing the Carrier DNA from 0 µg mL-1 to 400 µg mL-1, with salmon
sperm DNA being denatured before adding it to the sample (Shimogawara et al., 1998). The same was
not implied in this study, in which the SSD was not denatured before adding it to the mixture. It is possible
that without the denaturation step, essential for keeping the DNA single stranded, double stranded
fragments recombine with the genome, possibly impairing essential genes and leading to a decrease in
viability. In the case of N. oceanica, little is reported regarding the application of SSD with most articles
not using them, including Kilian et al., (2011) which has reported one the maximum transformation
efficiencies (Q. Wang et al., 2016; Wei et al., 2017). And when applied in N. oceanica, concentrations
37
are lower – Li et al., (2016) used 15 µg mL-1 and less than 1-fold to the DNA concentration (5:3; DNA:
SSD). These results conclude that the use of SSD at the tested concentrations in N. oceanica IMET1
does not help to increase the frequency of transformants.
After applying an electric pulse, medium is added to the cuvette to allow a fast cell recovery from
associated cellular damage, induced by electroporation. It was shown that C. reinhardtii cells held on
ice after the electroporation pulse had taken up more exogenous DNA than the ones at room
temperature (Brown et al., 1991). Since electroporation causes the temporary formation of pores,
keeping cells at lower temperature following the pulse could allow the pores to remain opened for a
longer time, therefore possibly increasing the uptake of exogenous DNA. This theory was studied by
analysing the differences in using recovery media at 25ºC, handled at room temperature and at 4ºC,
using ice, Figure 8 – (E). Standard deviation from the 4 replicates was too high to get significant
differences between the 2 groups, apart from the 4ºC average being slightly higher than the room
temperature. Once sample-to-sample variation is large with the electroporation plating procedures, more
sensitive approaches are necessary to study the influence of this parameter.
Dimethyl sulfoxide (DMSO) has a wide range of applications. From its use to inhibit DNA secondary
structures in PCR reaction mixes, as a cryoprotectant to reduce ice formation or as a carrier solvent
used for microalgae species with thick walls (Chen et al., 2009). The possibility of DMSO being also
used as a carrier solvent for DNA was evaluated by applying, 2.5%, 5.0%, 7.5% and 10.0% v/v with a
pre-treatment of 15 minutes before pulse application, Figure 8 – (F and G). The initial experiment
screened the 4 concentrations, n = 2 and SSD at standard concentrations, Figure 8 – (F). Results show
that there is a decline in transformation efficiency from the group 2.5% to 5%, 7.5% and 10% (350.0
±50, 107.5 ±92.5, 39.0 ±31 and 33.5 ±24.5, respectively). This seems to agree with DMSO toxicity
(Galvao et al., 2014). When using no DMSO, interestingly, only 39 ±6 CFUs were observed. Other
experiment was performed with a higher number of replicates (n=4) to assess the differences of 0% and
2.5% DMSO treatment and this time, without the addition of SSD, Figure 8 – (G). However, no relevant
results were observed. Therefore, at the tested conditions, without SSD addition, DMSO pre-treatment
at 2.5% did not enhance the frequency of transformants. Moreover, there is an indication that the use
of DMSO may decrease the inhibitory effect of SSD, once preliminary results (Figure 8 – F) showed
more transformants when using DMSO than without.
The parameters of electroporation after optimization are the use of a cell concentration of 5E9 cells mL-
1, a concentration of DNA of 1 µg per cuvette, no treatment with SSD and DMSO, and the use of recovery
media at 4 ºC.
4.1.2. Viability Assessment of staining and sorting using the FACS
Fluorescence Activated Cell Sorting is an efficient and widely used technique in medical and biological
research to differentiate cells for many different characteristics. However, one of the major limitations
comes to problems associated with sorting, where cell growth and viability can decrease due to shear
38
stress caused by the machine (Elliott, 2009). This phenomenon may cause limitations on research, for
instance on screening rare phenotypes, where a great number of cells are required.
To test the effect of staining with BODIPY and cell sorting, a single mutant colony originated with IC21
was grown in liquid medium until exponential growth. This colony was then used for testing the different
combinations of staining and sorting rounds – Figure 9. Single events were sorted in ultra-purity mode
to rectangular shaped agarose plates containing medium with the antibiotic zeocin.
Figure 9. Flow-scheme of the viability experiment. Samples can be identified for the number of sorting rounds as
S1, S2 or S3, and the staining condition as BDP (BODIPY stained) or UNS for unstained. Plate A (S1_UNS) was
done by sorting tube 1. Plate B (S2_UNS) derived from tube 2 (the latter sorted from tube 1). Plate C (S3_UNS)
derived from tube 3, following the same logic. Plate D (S1_BDP) derived from tube 4, previously stained with
BODIPY. A tube 5 was made for plate E (S2_BPD) and a stained version of it was performed for plate F (S2_BDP2),
the latter with a second round of staining (tube 6). Plate G (S1_UNS) was also done by tube 1 as plate A, with the
only exception of not containing antibiotic medium, therefore used as an antibiotic effect control. Agarose plates
were used with a 384 well sorting module and sorted in ultra-purity mode at one event per well.
Plates were grown in the low light chamber for 4 weeks and analysed by counting colony forming units
– Figure 10.
39
Figure 10. Cell viability on agarose plates after multiple rounds of cell sorting and BODIPY staining. Sorting was
applied at ultra-purity mode and plate G, without antibiotic, was performed in the same conditions as plate A.
Efficiency was calculated based on obtained CFU numbers and total sorted events (n=196).
It has been shown that sorting with the FACS causes shear cell stress due to hydrodynamic stress
(Elliott, 2009). Therefore, it could be envisioned that cells that are treated with several sorting rounds
could have less chances to survive. However, results from agarose plates A, B and C with respective 1,
2 and 3 sorting rounds did yield 34%, 31% and 35%, respectively. Not a large variation was found
between the 3 plates and no lower tendency is observed. Therefore, for the applied conditions, we
assumed that multiple sorting rounds does not affect cell viability.
For studying the effect of staining and sorting, BODIPY was applied at standard conditions. In the case
of staining, there is a clear drop in viability when comparing the unstained and one-time-stained samples
– plate A and D (34% to 20%, respectively), and plate B to F, unstained to two-times-staining (31% to
12%, respectively). No literature is found regarding BODIPY toxicity for animal or plant cells and it has
been considered non-destructive (Govender et al., 2012). However, BODIPY was dissolved in DMSO,
which has been classified as toxic for microalgae at low concentrations (Galvao et al., 2014). Therefore,
it is possible that the used concentration lead to a viability decrease after staining, which is even more
pronounced after the second staining, with only 12% of efficiency.
Plate G was used as a negative control for the effect of antibiotic on cell viability and used the same
conditions as plate A. Results show that this plate control had the highest observed efficiency (45%).
When sorted, cells must adapt to a different culture media, from liquid to agar. It could be possible that
shear stress induced from sorting together with the selective pressure of zeocin could influence the
ability of the cell to thrive in a different environment.
Similar results were achieved for sorting cyanobacteria where a cell viability of 25% was reported (Van
Dijk et al., 2010). Also, a study using cell sorting for plant protoplasts reported efficiencies of 24% and
39%, when using 76 µm and 100 µm flow nozzles, respectively (Harkins & Galbraith, 1984). As lower
tip sizes increase pressure in the sorting fluid, it is possible that the stress related to pressure could be
0
5
10
15
20
25
30
35
40
45
50
Plate A(S1_UNS)
Plate B(S2_UNS)
Plate C(S3_UNS)
Plate D(S1_BDP)
Plate E(S2_BDP)
Plate F(S2_2xBDP)
Plate G(Control)
Eff
icie
ncy %
(C
FU
/Tota
lSort
s)
Agarose plates
40
a contributing factor. As in this study a 70 µm nozzle chip was used, replacing it with a 100 µm or 130
µm chip could perhaps increase cell sorting viability, which is highly relevant to isolate single cell
colonies.
4.2. Screening a knockout mutant library for altered chlorophyll and lipid
contents
This thesis focused on the development of a pipeline for isolation and characterisation of gene knockout
mutants of N. oceanica IMET1 with interesting phenotypes. This organism has 9915 predicted genes
and a high gene density – 329 genes/Mb, when comparing to other microalgae species like
Chlamydomonas that has 149 genes/Mb (Merchant et al., 2007; D. Wang et al., 2014). Consequently,
IMET1 has a greater chance of a random insertion to cause a gene knockout. Also, many of its genes
are related to the photosynthetic apparatus and lipid accumulation – see Theoretical Background –
section 2.3. Therefore, it is expected that some of the generated knockouts will be genes that are related
to these two functions. Furthermore, we expect these mutant knockouts to have an impaired or
enhanced ability to produce chlorophyll and/or lipids and that the differences could be detected with the
FACS.
For screening gene knockouts, a random mutant library was created via transformation of cells with a
linear DNA cassette through the previously optimised electroporation protocol. Within this experiment,
approximately 8000 mutant colonies were counted visually while discriminating between the colony
material size – big, medium and small. False positives are very common on agar or agarose plates that
use antibiotics as selection markers. These are wild-type colonies that resisted the antibiotic selection,
either because of its degradation by light or due to inactivation of the antibiotic by resistant mutant
colonies, enabling the surrounding cells, named satellites, to grow around them. The latter is easily
visualized by a difference in colony size with the satellite colonies having less cell material. To avoid
miscounting false positives, 20 colonies of each size (big, medium or small) were re-plated into fresh
zeocin plates. Cells were grown for one week to discriminate between grown and bleached cells. Results
suggest that all big colonies are positives (100% efficiency), 8 in 10 medium colonies are positives (80%
efficiency) and only 7% in small colonies (n=20). CFU were counted visually and these ratios were
applied for counting the total number of the mutant library.
All agarose plates were flushed with NB medium and placed in Erlenmeyer flasks (level 2 and level 3
pools) at the same OD – See the more detailed protocol in Materials and Methods – section 3.6.1. Each
of the four level 2 pools (A/B, C/D, E/F and G/H) contained ¼ of the total mutant library – approximately
2000 mutants. The two flasks of level 3 pools, big pool (BP) and big pool diluted (BPD) contained all the
knockout library, with the latter being diluted 10 times. This diluted pool was created with the intent of
screening cells with altered phenotypes when cells are light saturated, once cultures were too diluted
for cell to cell shading to occur. All level 2 and 3 pools were grown for 2 days on the high light incubator
until reaching exponential growth state and were then screened for altered traits according to the
experimental flow scheme – Figure 11.
41
Two bioprocess phases that are well studied in photoautotrophic microalgae are exponential growth and
the application of nitrogen starvation for lipid accumulation. These two are important, for instance in a
biodiesel production platform, once high biomass quantities need first to be achieved in exponential
growth, followed by nitrogen starvation phase for inducing lipid accumulation. This results in a shift in
the carbon flux, mainly from protein synthesis to production of high energy storage compounds, mostly
TAGs (Banerjee et al., 2016). Therefore, to select mutant phenotypes in both exponential and starved
conditions, the knockout library pools were submitted to both phases. Screening in the exponential state
was performed first and is further mentioned Day0 (or D0). In the case of starvation, it was performed
for 2 days (Day1 and Day2), Figure 11 - step 1. For screening cells using the FACS, gates on the
different fluorescence populations had to be designed, Figure 11 - step 2. For every pool, minor gate
adjustments had to be performed before cell sorting. At last, on Figure 11 – step 3, 48-well microplates
were prepared with NB medium containing zeocin and 100 events of every gate were sorted into single
wells of the plates. All 18 microplates were then grown in the high light incubator for approximately 2
weeks.
Figure 11. Schematic representation of the mutant library screening for altered profiles of lipids and chlorophyll.
Selecting interesting phenotypes through a high number of knockout mutations has several associated
challenges that need to be considered. First, if mutations that cause severe alterations to phenotypes
are way less frequent than the number of non-relevant phenotype mutations, the representativity of the
first in a population pool might be too low for its selection and isolation. To counter this, an intermediate
step between pools and cell isolation is necessary. This can be done by narrowing through the
distribution of the fluorescence pattern. For instance, if high chlorophyll mutant producers are desired,
42
by sorting first a small number of events (such as 100) in the top chlorophyll fluorescence and by
analysing again the fluorescence spectra, chances are that alternate phenotypes will be better spotted,
due to a higher representation in the distribution when comparing to the initial pool. The use of a high
throughput sorting system that is the FACS makes this process simpler, especially when using it with
microplates.
The sorting gates were corresponding to the 6 regions of the fluorescent population – 0.05%, 0.5% and
5% of the top and low populations of chl, Figure 12 – (A). Same gates were applied for the top lipid
populations, with the exception for low, where only 0.5% was applied, Figure 12 – (B). A medium lipid
gate was also selected and used as a control. Each microplate contained both chl and lipid altered
sorted phenotypes. Also, high chlorophyll and high lipid events were screened for all days. With
exception for low phenotypes, which were solely screened in Day0. Microplate sorting layouts can be
found in Supplementary File 3.
Flow cytometer analyses for chlorophyll were performed using the 720/60 nm (FL5) channel and for
lipids the 525/25 nm (FL1). Both fluorescence channels were plotted with Forward Scatter (FSC) on the
X axis, which is commonly assumed to be an indication for cell size. As larger cells have also larger
amounts of lipids and pigments like chlorophyll, higher fluorescence intensities are achieved for bigger
cells. Therefore, to not neglect potential mutants with a small cell size and high lipid/chl content, gates
were designed along the slope of the scatter plot.
Figure 12. Gates designed for screening chlorophyll and lipid altered phenotypes of the mutant library. Y axis on
both A and B were plotted in logarithmic scale. Gate percentages were adjusted in every sample to achieve a close
percentage of 5%, 0.5% and 0.05%, low or top percentages. Figure retrieved from the cell sorter software.
43
Among 2000 or 8000 gene knockouts, it is expected that some mutations confer different fluorescent
phenotypes. For example, if a transcription factor, important in the regulation of the photosynthetic
apparatus gets compromised, it is expected that a different fluorescent pattern is observed at the FL5
channel, which represents mostly chlorophyll-a fluorescence (Blaustein, 1992). The frequency of these
mutations among the gene knockouts is unknown, as well as the impact that these have on the
fluorescent patterns. In addition, it is also possible that gene knockouts with great influence on
chlorophyll or lipid accumulation could cause more prominent fluorescence shifts than less relevant
genes. This might result in different phenotype strengths. Hence, by selecting the top or low 0.05%
gates, chances are that more prominent phenotypes will be more frequent in the extreme regions than
in the top or low 5% gate. Therefore, the reason behind gates in the different regions (0.05%, 0.5% and
5%) was to enhance the chance of finding both weak and strong phenotypes.
Microplates were grown in the same conditions as the pools for 2 weeks. After this period, phenotypes
had to be confirmed and isolated into agarose plates using the FACS – Figure 13. A large variability in
OD was observed across the microplates. Since cell density could influence single cell lipid and
chlorophyll contents, OD was adjusted by diluting the most concentrated wells. This was performed
visually based on the colour of the wells since the number of samples (48 x 18 microplates = 864 wells)
was too large for taking exact spectrophotometer measurements. To confirm phenotypes, microplates
had to be submitted to the same conditions as the sorted level 2 and level 3 pools. D0 microplates were
screened with cultures during exponential growth. D1 and D2 plates were centrifuged with a microplate
centrifuging set, NB medium was discarded and replaced with nitrogen free NB medium – Step 1, Figure
13. For analyses, Figure 13 – step 2, samples of each 2 columns in every microplate were screened
per each day. Not all the wells were run due to time limitation. Samples for chlorophyll were analysed
non-stained and lipid samples by using the standard staining procedure with BODIPY. A total of 100.000
events were screened for every sample with a maximum EPS of 2.000 using a 70 µm nozzle. The
fluorescence in the FL1 and FL5 channels were compared to control samples using histograms.
Samples with a higher or lower average fluorescence than all the controls and samples resembling
skewed or bimodal distributions were isolated to agarose plates in single-cell-mode using a 384-sorting
module (Figure 13 - step 3).
44
Figure 13. Experimental flow scheme of altered phenotypes confirmation and isolation. Step 1 – microplate
incubation for 2 weeks, followed by media replacement of Day1 and Day2 plates for nitrogen starvation. Step 2 –
mutant phenotype analyses for confirming altered profiles in each sample well. Step 3 – mutant sorting and isolation
in single cell mode of confirmed phenotypes.
For phenotype analyses, two controls were sampled from each microplate – one for lipids and other for
chlorophyll analyses. Both controls were created during the first round of screening by sorting 100
events on the medium fluorescence of the chlorophyll or lipid population. In total, 5 controls were
analysed for each condition and day. Also, microplate wells with sorted low fluorescence events were
analysed during Day0 and Day1 for chlorophyll and during Day0 for lipids. Low chlorophyll samples
were run in the second day of starvation for observing potential interesting phenotypes. However, in
Day2 microplates, no growth was observed for wells with previous low chlorophyll sorted events. In
addition to this, the number of wells containing high chlorophyll sorted cells that grew was higher than
the number of wells containing low chlorophyll sorted cells. It is possible that events with low FL5
fluorescence could be associated to cells with a compromised viability. In addition, a lower number of
lipid-screening wells showed growth when comparing to chlorophyll. This could be associated to
staining. As seen in the sorting viability experiment, it reduces viability by half when comparing to
unstained samples. Also, wells with contamination and poor growth were analysed but discarded for
interpretation due to inconsistent data. For this reason, the number of screened wells for lipids were
lower than for chlorophyll.
Results of microplate analyses and isolated colonies on agarose plates were treated using the software
Rstudio, based on R, using Bioconductor and Flowcust dedicated functions. Median of the fluorescence
data collected in the different channels was calculated and used to make barplots. Controls are identified
with the letter C followed by a numeric sample identifier (1 to 5). In the case of the mutant phenotypes,
a numeric identifier was attributed to facilitate visualization. These were correlated to the sample
45
identifier and can be found in Supplementary File 6. Flow cytometry analyses of microplate sorted events
of level 2 and level 3 pools can be visualized in Figure 14.
Figure 14. Flow cytometry analyses of microplate sorted events for chlorophyll altered phenotypes. Plotted data are the median values of FL5 channel. Error bars correspond to the interquartile range (IQR; 50% for the positive and 50% for the negative).
46
Figure 15. Flow cytometry analyses of microplate sorted events for lipid altered phenotypes. Plotted data are the median values of FL1 channel. Error bars correspond to the interquartile range (IQR; 50% for the positive and 50% for the negative).
47
In Figure 14, for chlorophyll events sorted in the growth exponential condition (Day0), no significant high
chlorophyll producing phenotypes were observed between the controls. Also, when looking at low-sorted
wells (coloured in yellow), no lower fluorescence than the lowest control was observed, having sample
35 and 38 even a bigger intensity than the top control. Results are then not in fully agreement with the
applied sorting gates. Not all the average fluorescence signals agree with the applied gates for sorting,
but also, the contrary was not expected. For a knockout mutant to display an altered phenotype, a gene
that strongly influences production of chlorophyll or lipid accumulation needs to be interrupted. Since
not all genes are related to these two traits and since not all the genes that are, may induce a visible
phenotype, chances may be low to sort 100 events of interesting knockouts over 2000 or 8000 mutants.
In the case of starved cultures (Day1 and Day2) low and high chl-sorted events had no differences
between the controls for both days of starvation. By comparing starvation and exponential results is
possible to observe that the variability in fluorescence intensity between samples was lower for
starvation. This might be due to the decrease of fluorescence from Day0 to Day2, where a slight drop
in FL5 fluorescence was observed from Day0 to Day2. After 2 days of starvation the colour of cultures
changes from green to slightly yellow, suggesting a drop in the chlorophyll content. As suggested in
literature, this phenomenon might occur due to chlorophyll being targeted for degradation for nutrient
recycling under nutrient depletion, once chlorophyll is rich in nitrogen (4 atoms per each molecule) and
is one of the most easily accessible pools of nitrogen (Y. Li, Horsman, Wang, Wu, & Lan, 2008). And
possibly due to chlorosis, a phenomenon where the production of chlorophyll is insufficient (Prado,
Rioboo, Herrero, & Cid, 2011). In addition, reactive oxygen species (ROS) are linked to light stress due
to the photoreduction of O2 to the superoxide radical. This can happen when cells are exposed to light
intensities that surpass the capacity for CO2 assimilation, therefore light saturated, which leads to an
overreduction of the electron transport chain and consequent inactivation of PSII and inhibition of
photosynthesis (Apel & Hirt, 2004).
In the case of microplate wells screened for lipids, Figure 15, there is a larger sample-to-sample
variation, in a magnitude of almost 4-fold when comparing to the 2-fold maximum difference in
chlorophyll. In addition, error bars were also too large for visualizing clear differences between controls
and mutant samples for all the days. Sample-to-sample variation could be caused by the presence of
residual nitrogen medium after the medium discarding step. Consequently, cells would have still
available nitrogen that would delay the induction of lipid accumulation. In the case of the average
fluorescence intensity of samples in Day0 and Day1, no significant differences are observed.
Consequently, after 1 day of starvation no increase in lipid content could be consolidated. However, at
Day2, an increase of approximately 1.5-fold in the general fluorescence of FL1-A channel is observed.
Consequently, starvation has only promoted lipid accumulation on the second day under nitrogen
depletion.
Samples screened for mutations at the lower lipid populations have a major drawback. Since this
fluorescence is given by the efficacy of staining, gating cells for lower fluorescence is hindered by cells
that were not stained, yielding a low green fluorescence events at the FL1-A channel, Figure 16.
48
Figure 16. Influence of staining with BODIPY in the FL1 channel of a microplate sample screened for lipid analyses.
A “tail” is observed in the black square with events at lower green fluorescence. Data is plotted for the FL1-A in the
X axis logarithmically and for FSC-A in the Y axis.
One limitation of staining with lipophilic dyes is the thick and robust cell wall that the majority of
microalgae species have, especially N. oceanica (Rumin et al., 2015). This acts as a barrier, hampering
BODIPY from penetrating into cells and staining lipids. To improve staining efficiency, solvents like
DMSO can be used to enhance permeability of dyes. Although the staining procedure with DMSO was
optimized for staining time, cell concentration, DMSO and BODIPY concentration, there were still non-
stained cells represented at the lowest FL1-A in the black square, Figure 16. This greatly hinders the
possibility of finding mutant cells with altered low lipid phenotypes in the 100 sorted events. To worsen
this scenario, the staining procedure decreases viability in 2-fold when comparing to screening in non-
stained samples. Therefore, a different strategy for screening low lipid producers is necessary. One
solution could be the application of several sorting and staining rounds to get rid of the non-stained cells.
Since cells keep BODIPY inside the cell, by sorting and staining twice, the number of low FL1-A events
related to non-stained cells, decreases (data not shown). However, since a great number of cells are
lost during staining, it is necessary to start with the highest possible number of events.
In regard of the high sample-to-sample variation, a more careful approach is necessary for removing
the remaining medium. In this study, microplates were centrifuged, and medium was discarded by
flipping over microplates. However, an alternative and more precise technique is necessary, for instance
by carefully pipetting each well. Another solution to decrease error would be to increase the volume of
added media, by for instance using 24-well microplates instead of the 48.
As observed before in Figure 12 (demonstration of the applied gates for sorting level 2 and level 3 pools),
visualization of fluorescence over forward scatter is essential to get rid of high fluorescence related to
larger cell sizes. To test how this distribution looks like in the microplate analyses, samples 1, 2 and 3
49
of chlorophyll screening for Day0 were plotted for FSC-A and FL5 channels in a density plot – Figure
17.
Figure 17. Chlorophyll events over forward scatter in phenotypes of microplate screened samples in Day0. These
samples can be visualized in barplots in Figure 14.
It can be observed that all the samples have a positive slope in the distribution, which is expected since
bigger cells have also more lipids and pigments. However, the three altered phenotype samples have a
distribution with a tail in the highest fluorescence respective also to higher FSC, while in the C5 a normal
distribution is observed. These samples could be related to knockout mutations that confer higher
production of chlorophyll. Though, in most of the cases this tendency disappeared after confirming the
phenotypes a few hours or days later. Also, these phenotypes have a higher number of cells with big
size in comparison to the small. This could be associated to a non-synchronisation of the cell cycle, in
which the cell size is strongly related, as cells approaching cell division increase in size. These changes
complicate the search for relevant mutations, since altered phenotypes might pass unnoticed.
All interesting phenotypes were gated and sorted in isolates along one or more column numbers to an
agarose plate with the 384 well-microplate module. Sample identifiers of isolated colonies can be
accessed in Supplementary File 7. Single isolated mutant cells were grown for 3 weeks in the low light
incubator. After this time, it was observed that a low number of colonies was obtained, especially in lipid
analyses. The fact that lipid staining lowers cell viability agrees with these results also. Colonies were
then inoculated in 48 well microplates in the high light incubator and analysed using the FACS – Figure
18.
50
Figure 18. Flow cytometer analyses of cells isolated from interesting phenotypes of microplates in Day0 chlorophyll
and lipids. All samples in chlorophyll relate to sorted high fluorescent phenotypes. In lipids, samples 17 and 4 relate
to sorted high sorted and samples 2 to 5 for low sorted phenotypes.
Results show that there are no relevant differences between controls and samples, for both chlorophyll
and lipids. This indicates that possibly no relevant mutations that alter chlorophyll and lipid production
(FL5 and FL1 channels, respectively) were in the isolated knockouts. On other hand, it is also possible
that sample-to-sample variation is too high for these mutation phenotypes to be visualized clearly. Since
N. oceanica has a gene density of 329 genes/Mb, a mutant library with approximately 33.000 mutant
knockouts would cover all the genome. However, the screening library had only ¼ of the genome
coverage number, with approximately 8000 mutants. In this regard, it is ultimately possible that the lack
of altered phenotypes is also associated to a low number of knockouts.
Another factor that might add to the sample-to-sample variation is the influence of cell density, and the
way it can affect chlorophyll and lipid composition at intra-cellular level. To test this idea, WT material
from an agarose plate was inoculated in liquid medium using a 48-well microplate and cultivated during
3 days, with 3 different quantities of cell material and 3 respective dilutions with a 10-fold dilution factor
– Figure 19. Inoculated biomass was taken based on empiric observation for the 3 categories – small,
medium and high amount of colony material.
51
Figure 19. Influence of cell density on chlorophyll and lipid fluorescence spectra of WT cells. (+) little cell material;
(++) medium cell material; (+++) big amount of cell material. Dilutions of 10-fold were made for every sample
condition.
Results reveal that cell density affects the FL5 and FL1 channels with a similar degree of variability as
for samples in Figure 18. In chlorophyll there is a slight association to higher cell densities and more
intense fluorescence. At densities where cells are light-saturated, meaning that the level of
photosynthesis is maximal, the effect of cell to cell shading is not relevant. When cell density increases,
shading occurs and the amount of absorbed light per cell decreases, therefore decreasing
photosynthetic activity. As cells can adapt the antenna complex depending on the light that is received
(Masuda et al., 2003; Neidhardt et al., 1998), it is expected that the amount of produced pigments also
increases. A larger antenna complex results in a larger amount of chlorophyll per cell therefore possibly
increasing the FL5 fluorescence. Consequently, this influence compromises a high throughput of
screening, since adjustments of all the microplate wells are too time consuming.
In the case of lipids, the same behaviour is observed only in dilutions of 10 (more lipid accumulation
with bigger cell densities). But no differences were observed between non-diluted samples. In theory, it
might be possible that different cell densities interfere with lipid accumulation, due to differences in the
metabolic flux and nutrient uptake rates. Also, a greater nutrient consumption might cause nutrient
depletion, which could trigger an increased lipid production.
52
4.3. Development of a fluorescence – based selection for IMET1
transformants
One of the objectives of this project was to efficiently select a random knockout library. For this,
parameters that affect the antibiotic selective pressure are usually optimized in regard of its
concentration. However, instead of using solely antibiotics as selection, this study explored a different
approach, by trying a fluorescence and cell sorting based methodology. The idea of using fluorescence-
activated cell sorting for screening cells based on fluorescence is not new, however, nothing has been
so far published in using this method for fluorescent mutant selection after transformation. When working
with other organisms such as bacteria or yeast, selection in antibiotic is fast due to the organisms short
doubling times. However, microalgae have a slower doubling rate, which makes selection in solid media
to take 2-3 weeks. With the aim of decreasing selection time, the idea of transformants being picked
one day after electroporation procedure was tested.
4.3.1. Construction of IC16, IC17 and IC18 and screening for high fluorescence
To test this new method, the plasmids pNIM16, pNIM17 and pNIM18 with fluorescence markers mKO2
(max abs. 551 nm), mCherry (max. abs 587 nm) and GFP (max. abs. 488 nm) were created,
respectively. From all the fluorescent proteins, GFP absorbance is the most adequate for the blue laser.
Elements that are common for the insertional cassette of plasmids are the VCP promoter, its first intron
splicer and the terminator. The different IC elements can be visualized in Figure 20.
Figure 20. Structure of Insertional Cassettes 16, 17 and 18, containing fluorescent proteins mKO2, mCherry and
GFP, respectively. Bar sizes are not in the real base pair scale.
To make sure that fluorescent proteins were expressed, zeocin resistance gene was put in the
downstream region of the cassette, with a cleaving sequence peptide between the 2 selection markers.
Fluorescent proteins mKO2 and GFP were codon-harmonized and ordered as synthetic gene fragments,
with an overlapping region containing a part of the P2A sequence. The other half was constructed with
a long primer targeting the 5’ region of bleR gene. After amplification of the bleR and the backbone by
PCR, constructs were assembled together with the gene blocks. For mCherry the same was performed,
except that it was amplified with a reverse primer containing part of the P2A sequence. E.coli
53
transformed colonies with the assembled constructs were verified by colony PCR. Colonies with the
expected fragment length were inoculated in liquid LB medium and plasmids were purified. After
digesting plasmid DNA, those with correct digestion map were sent for sequencing for validation. After
this, correctly assembled plasmid DNA was digested, diluted and used as a template for preparative
PCRs.
N. oceanica wild-type cells were transformed with the standard protocol and plated first on agarose NB
medium containing zeocin. Colonies grown in zeocin plates indicate that cells could effectively integrate
the cassette and express the zeocin resistance gene. For this to be possible, the fluorescent proteins,
placed at the upstream of the bleR, need to be also translated. After 2-3 weeks of incubation time, all
the insertional cassettes yielded transformants. Colony material from these was picked from the agarose
plate and cultivated in liquid medium in the high light incubator, together with a wild-type control, isolated
also from an agarose plate at similar growth conditions. Samples were taken while in exponential growth
and analysed using the FACS. Results can be seen in Figure 21.
Figure 21. Fluorescence distribution of IC16, IC17 and IC18 isolated colonies compared to a wild-type control.
Image retrieved from the Sony® Cell Sorter software.
From the results, it is possible to observe that IC16 fluorescence, relative to mKO2 transformants was
overlapping mostly with the control. However, a slight advancement of the mutant suggests a higher
fluorescence, possibly due to mKO2. As mKO2 absorption spectra has a maximum fluorescence peak
at 548 nm and since the blue laser excites at 488nm, only 17% of the maximum fluorescence intensity
is achieved (Karasawa et al., 2004). As a low absorption leads to low fluorescence emission, the emitted
fluorescence by kusabira orange could possibly not be enough for observing differences against the
control. The same was observed for mCherry, where no relevant results were obtained. For, GFP a
clear difference between the control is observed at the expected fluorescence channel (525 nm).
Colony PCR was performed for further confirmation of the results, by using DNA template from a pool
with 8 colonies of each insertional cassette (IC), grown in liquid NB medium with zeocin. Forward primers
54
targeting fluorescent proteins were designed specifically for each cassette and a reverse primer
targeting a region of the TATUB was used for both ICs. Negative controls of each IC samples were used
with the same conditions except for the template DNA, which was taken from a wild-type culture grown
at similar conditions as the mutants. As positive controls for the extraction protocol, primers targeting
pVCP gene with mutants and WT DNA were used. The expected band sizes were confirmed by running
an electrophoresis gel (1% agarose) – Figure 22.
Figure 22. Colony PCR results of IC16, IC17 and IC18 colonies. Digestion and amplification with Phire polymerase
kit. One kilobase ladder was loaded in the 1% agarose gel on both ends. A – samples A to K; (B) – samples L, M
and N, which are a repetition of samples J and K, although with different template dilutions; (C) – sample well
identifiers.
In Figure 22 – (A), all the mutant samples (A, C and E) had a band at the expected size. Positive controls
with pVCP primers successfully worked for both IC, with a small note for IC18 (lane I) that had a faint
band. A weaker band is also reported for the IC18 mutants on lane E, which agrees with a lower DNA
extraction, reported by the respective pVCP positive control. Both IC negative controls didn’t yield any
band also as expected. However, samples J and K, related to the positive control of the WT were
supposed to have bands at a similar size of the samples G, H and I but no bands were visible. This
could have been related to the DNA template concentration. As so, positive controls of the WT were
repeated with the same DNA template, but in different dilutions, Figure 22 – (B). Sample L was touched
with pipette and was considered the more diluted (+++). Sample M was 1/100 dilution of sample J and
K (dilution ++) and sample N the same DNA quantity as J and K (dilution +). Results indicate that the
used concentration in Figure 22 – (A) for the positive controls was too high for PCR to work successfully.
As for the diluted versions, both worked and bands appeared at the expected size. The dilution ++ was
further used as a template with IC primers for a second validation (data not shown). Results demonstrate
the presence of the mKO2, mCherry and GFP integration in the genome of N. oceanica transformants.
55
4.3.2. A mutant selection approach based on fluorescence of IC18
Previous results have shown that N. oceanica IMET1 colonies containing a GFP expression marker
(IC18) have a higher fluorescence intensity on the green channel than non-transformants. Therefore,
this fluorescent marker seemed appropriate to study its feasibility as a selection marker.
Wild-type cultures were harvested in exponential growth and with a QY of 0.70. Cells were washed and
10 samples containing IC18 were transformed with the optimized electroporation protocol. After the
pulse, samples were pooled together to avoid sample-to-sample variation and recovered overnight in
the high light incubator. Samples were then centrifuged and 10% of the volume was plated on an
agarose plate containing NB and zeocin, Figure 23 – plate A. The remaining volume was used for the
first round of sorting. For this, gates were designed for the top 2.5% of the FL1-A fluorescence plotted
against FSC-A. In the first round, since the sorted speed was at around 120.000 events per second, the
formation of doublets and triplets comprised 25-30% of the complete cell gate (high EPS produce great
quantities of doublets and triplets). Therefore, two sorting rounds were necessary – the first to select the
mutants and doublets & triplets out of the large wild-type population (approximately 5E9 cells mL-1) and
the second to only select the mutant events. The top 2.5% gate and doublets and triplets were gated
and sorted in the first round, in semi-yield mode. Cells were centrifuged and resuspended in Tube 1,
which was then placed overnight in the high light incubator for recovery of sorting associated stress and
fluorescence stabilization. One tenth of Tube 1 volume was plated on agarose plate B, as performed for
plate A. The 90% was then used for the second sorting round, operated at lower EPS (approximately
30.000) and do not accounting doublets and triplets. For this, a gate in the top 5% FL1-A fluorescence
plotted against FSC-A was designed. Sorting round 2 was also operated with a semi-yield sorting mode.
Tube 2 was then centrifuged and all the pellet was plated on agarose plate C, with NB and zeocin.
Plates were grown in the low light chamber until colony formation.
56
Figure 23. Experimental flow scheme of a fluorescence-based approach for selection of IMET1 transformants
Running samples at high speed requires highly concentrated samples, which consequently lead to
formation of clumps and cell-to-cell aggregation. It was detected that when samples were too
concentrated, and aggregation was visible, the machine EPS would drop to zero due to clogging,
possibly in the sample line. By pausing and resuming the FACS the sample pressure would change and
unclog. However, this would require a full-time operation of the equipment and possibly leading to chip
failure due frequent clogging. To avoid this, EDTA was used and at a concentration of 6 mM, no variation
in cell viability clogging of the machine was observed (data not shown).
The first agarose plate (A), created the day after electroporation and before both sorting rounds was
used as a reference for a maximum efficiency scenario, which was further used to compare with the
obtained data from plate B and C. For example, an efficiency of 100% in the sorting round 1 would only
be achieved if the cell counts of plate B would match the non-sorted counts in plate A. In the second-
round the value of obtained data of the first round is used as a maximum scenario. Results can be
accessed in Figure 24.
57
Figure 24. GFP mutant selection results after two rounds of sorting using the FACS – Sorting round 1 (S1) and
Sorting round 2 (S2).
Plate A yielded 560 colonies. Based on this value and the volume that was sorted in the FACS, a
maximum efficiency of 1260 was plotted. However, results of plate B revealed 450 CFUs in the first
sorting round – an efficiency of 36%. In the second round, only 10 colonies were achieved, out of the
theoretical maximum 450 value – an efficiency of 2.2%. Results suggest that the gates agree with the
designed gates without further fluorescence development of the transformants – S1 with up to 30% top
FL1 of the total “Cells” events (see gate hierarchy in Materials and Methods– section 3.4) and S2 with
FL1 top 2.5%. With one day of overnight recovery after electroporation, it could be possible that the
expression level of the insertional cassette was not enough to obtain a distinct fluorescence between
mutants and WTs. Additionally, as results suggested in the sorting viability experiment, a decrease in
viability of 50% could compromise the use of the FACS as a selection method. However, accounting the
decrease in viability and a lower designed gate in S1 (30% gate compared to 36% efficiency), it is
suggested that the distribution of mutants might be in the top of the FL1 fluorescence.
In the case of sorting round 2, only an efficiency of 2.2% was observed. As the sorting gate was 5% of
the top fluorescence, this suggests that the distribution of the mutant fluorescence on the FL1 channel
was similar to the WT population. Cultures after the first sorting round were centrifuged and resuspended
in lower amounts of volume in Tube 1 - Figure 23. However, its cell density was more diluted than in the
overnight recovery tubes. As so, a different cell dilution could have altered light sensitive pigments,
1260
450450
10
0
200
400
600
800
1000
1200
1400
S1 S2
Colo
ny f
orm
ing u
nits (
CF
Us)
Theoretical Maximum Yield Obtained CFU
58
changing FL1 signal. Differences in this channel and in FL2 were visible from the sample analysed
before sorting and samples analysed on the day after sorting – Figure 25.
Figure 25. FL1 and FL2 channels before and after sorting, analysed in the day of sorting round 1 and the day after, relative to sorting 2, respectively.
Samples ran before sorting had a greater fluorescence intensity in both FL1 and FL2 channels when
comparing to the after sorting sample. BS was screened after electroporation recovery, where cultures
were diluted to an approximate OD of 1. AS was incubated after sorting the top 30% to Tube 1, which
was concentrated to a lower OD of samples BS. As stated before, it is possible that changes in
fluorescence might be due to the different dilution rate or even the effect of sorting.
Another factor to consider is the fluorescence variation while handling cultures for the experiment. To
test this, samples that were ran with a difference of 1 to 2 hours were plotted – Figure 26.
59
Figure 26. Green fluorescence variation on samples screened before and after sorting. Before sorting (BS) was
analysed on the day of the first round; after sorting (AS) samples were analysed the day after, before sorting round
2. The numeric value 1 and 2 relate to the time of sample analyses, being samples 1 screened one to two hours
before samples 2.
From Figure 26, where FL1 is plotted over density, it is observed that BS1 has a slightly higher number
of events with greater fluorescence than BS2, suggesting a regression in fluorescence. However, this
effect was not seen in AS samples. Thus, fluorescence variation in the FL1 channel observed in a short
time might compromise the selection of high fluorescent phenotypes. Consequently, once cells were
handled during approximately 5 hours, it is highly possible that fluorescence regression could have
impaired mutant selection in both sorting rounds (S1 and S2). The use of other fluorescent proteins with
wavelengths emissions where this effect is not observed could be a solution.
During operation of the machine there were several factors that compromised the fitness and
reproducibility of the technique. There was a variation in all the fluorescence channels among change
in the EPS when increasing/decreasing substantially (around 10-fold) by regulating sample pressure,
which leads to gate disadjustments. The high number of doublets and triplets when EPS increases over
10.000, especially at around 100.000 EPS, that accounts 20 to 30% of the total gated cells. And, at last,
since N. oceanica IMET1 electroporation protocols require very concentrated cells (5E9 cells mL-1), a
long time is necessary to run an entire electroporation sample – one mL of sample at standard
concentrations would take 11 hours to run at 100.000 EPS, this for the first round of sorting. Therefore,
analyses and sorting speeds are too low to make this process more time advantageous in comparison
to plates. However, in the case of Chlammydomonas, where concentrations of 1E8 per mL are enough
for a solid transformation (Brown et al., 1991), sorting at the same speed would run 1 mL of sample in
0.28 hours. Therefore, considering 0.2 mL per sample, a total number of 144 samples could be run
during 8 hours of operation time.
Exploring the use of fluorescence for selection of mutant cells has several advantages over the use of
traditional agar plates. Slow-growth microorganisms take long periods of time until achieving sufficient
60
colony material for picking (some up to a month); the use of antibiotics has also drawbacks – light
sensitivity, false positives, high metabolic burden and changes in metabolomics (Ge, Chen, Qiao, Lin,
& Cai, 2009; Martinez, 2009); and colonies grown on agar plates need to be flushed with liquid media,
a procedure that can be time consuming, depending on the number of plates. Therefore, by selecting
cells with liquid media using the cell sorter, no preparation of plates, plating and flushing is necessary,
while the use of antibiotics can still be used as an additional step for getting rid of potential wild-type
events that were wrongly sorted with the FACS. In addition, the use of fluorescent proteins can be also
applied as a high-throughput method for quantifying gene expression.
61
5. Conclusion and future perspectives
The production of random mutant knockouts through electroporation could benefit from a more
optimized procedure. Results show that differences in exponential and late exponential cultures are
crucial for obtaining good results, with the first enhancing the number of transformants. In the case of
DNA carriers, these are often used in yeast specially to avoid DNA degradation by nucleases, increasing
the transformation efficiencies. However, in this study the use of salmon sperm DNA compromised the
transformation. Other parameters showed no relevant differences between the sample groups,
especially due to a high of sample-to-sample variation that is associated to the long electroporation
protocol. Consequently, less sensitive methods for testing electroporation parameters could be applied.
For instance, to test cell wall/membrane permeabilization, DNA fluorescence probes could be utilized.
Cell viability experiments of staining and sorting with the FACS revealed that the use of plates with
antibiotic decreased viability when comparing to screening in non-antibiotic plates. Furthermore, while
sorting multiple times has no effect on cell viability, staining with BODIPY diluted in pure DMSO results
in a 2-fold decrease. Lowering DMSO concentration or finding an alternative cell wall/membrane
permeabilizer might be considered since DMSO is highly toxic for microalgal cells.
Screening altered phenotypes of IMET1 was firstly performed by flushing a mutant knockout library from
the agarose plates. Mutant pools were created and sorted into microplates for high and low fluorescent
phenotypes. Analyses of microplate-wells revealed a high sample-to-sample variation from the controls
but with some wells containing irregular fluorescence patterns, possibly resembling phenotypes derived
from mutations. Interesting events were isolated in single cells to agarose plates and grown for 3 weeks.
By screening the isolates, grown a few days in microplate wells, phenotypic differences were either not
detected or were related to the changes in cell density or differences in cell size, possibly associated
changes in the cell cycle. These two factors greatly compromise the observation of altered phenotypes
associated to gene knockouts. As potential solutions, OD adjustments could be carefully performed
using less microplate wells, while for the different cell cycles, methods to synchronize cultures could be
attempted.
In the case of mutant selection, this study attempted a selection approach by using fluorescence proteins
as a selective marker and cell sorting. Three constructs carrying different fluorescent proteins were
designed and used to transform N. oceanica WT cells. From the 3 constructs, IC18 showed a clear
difference in fluorescence against the wild-type control. This cassette was then used for employing a
fluorescence selection strategy. Results showed that is possible to select mutant cells by using cell
sorting. However, efficiencies were very similar to the cell gates percentages, suggesting that one day
after electroporation, the fluorescence population of mutations was not much greater than the normal
distribution of WT cells, in the first sorting round. In this regard are necessary studies to enhance mutant
fluorescence, for instance by increasing gene expression with stronger promoters or other set of introns
in the insertional cassettes.
62
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7. Supplementary Material
7.1. Supplementary File 1. PCR settings and purification results of the 3 genomic
constructs used for ligation of all plasmids
Table 4. Purification of the genomic constructs sh ble, backbone and mcherry, previously amplified.
Sample Purification
condition
Elution
volume
(µL)
Concentration
(ng µL-1)
A260/A280 A260/A230
mcherry 1 PCR vial with one
collumn 10 158,4 1.80 1.83
Backbone (including
promoter and
terminator)
3 PCR vials and 1
collumn 15 410.2 1.87 2.25
Sh ble 2 PCR vials and 1
collumn 15 299.4 1.88 2.02
Figure 27. DNA amplification of sh ble gene, backbone and mcherry used for Gibson assembly
70
7.2. Supplementary File 2. PCR amplification settings and purification for all
insertional cassettes
A-H I - P Q - X
IC16 IC17 IC18
Figure 28. DNA amplification of IC16, IC17 and IC18.
Table 5. Purification results of IC16, IC17 and IC18 with the Thermofisher purification kit.
Sample Purification Condition Eluted volume
(µL)
Concentration
(ng µL-1) A260/A280 A260/A230
IC15 2 samples purified with 1
collumn 10 491.7 1.85 2.33
IC16 8 PCR vials purified with 2
columns 20 404.2 1.82 2.24
IC17 6 PCR vials purified with 2
columns 20 253.2 1.8 2.22
IC18 7 PCR vials purified with 1
column 20 295 1.8 2.25
71
Figure 29. Amplification of IC21 with both Phire and Q5 polymerases. A faint band in sample L was due to a mistake while loading the gel.
Table 6. Optimization of the IC21 DNA purification using two polymerases and two purification kits.
Sample Purification
condition
Concentration
(ng µL-1) A260/A280 A260/A230
Quantity of
DNA per
collumn
(µg)
Yield of DNA
per polymerase
(ng µL)
A 1 pcr vial q5
(thermo) 123.9 1.80 2.12 6.2 123.9
B 1 pcr vial q5
(zymo) 104.2 1.81 1.93 5.2 104.2
C 2 pcr vial
q5 (thermo) 155.5 1.81 2.04 7.8 77.8
D 3 pcr vial q5
(Thermo) 192.8 1.81 1.98 9.6 64.3
72
Sample Purification
condition
Concentration
(ng µL-1) A260/A280 A260/A230
Quantity of
DNA per
collumn
(µg)
Yield of DNA
per polymerase
(ng µL)
E 0.5 pcr vial phire
ZYMO 67.8 1.77 1.44 3.4 150.7
F 0.5 pcr vial phire
ZYMO 67.5 1.81 1.05 3.4 150.0
G 1 pcr vial phire
ZYMO 108.2 1.80 1.41 5.4 108.2
H 1 pcr vial phire
ZYMO 97.4 1.83 1.45 4.9 97.4
I 2 pcr vial
q5 ZYMO 122.6 1.82 1.91 6.1 61.3
J 3 pcr vial
q5 ZYMO 121.5 1.79 1.57 6.1 40.5
73
7.3. Supplementary File 3. Screening experiments – microplate layout used for
sorting
A
B
Figure 30. Microplate sorting layout for lipids and chlorophyll fluorescence events in screening Day 0 (A), and Day1 and Day (B) pools. L, M and H from the first letter stands for low, medium and high, respectively. Second letters L or C stand for lipids and chlorophyll, respectively. Sorted populations of 5%, 0.5% and 0.05% of respective population area were sorted based on FL1 or FL5 plotted against FSC-A. Images retrieved from Cell Sorter Software (Sony Corporation ®).
74
7.4. Supplementary File 4. Screening experiments – example of plate layout for
cell isolation
Figure 31. Example of a 384-well agarose plate used for cell isolation of interesting phenotypes related to high chlorophyll Day1 pools. D1 relates to the starvation day, the second identifier to the pool name and the third, the microplate analysed well. Events were sorted in single-cell-mode using the FACS.
75
7.5. Supplementary File 5. Fluorescence- based selection – Gates applied for cell
sorting
Figure 32. Gate used for isolation of high fluorescence events in the FL1 channel respective to the sorting round
1. (A) Gate High Fluorescence (HF) comprising 2.5% of the top fluorescence population without doublets and
triplets. (B) Doublets and triplets gate comprising ~30%.
Figure 33. Gate used for isolation of high fluorescence events in the FL1 channel respective to the sorting round
2. Gate High Fluorescence (HF) comprising 5% of the top fluorescence population without accounting doublets
and triplets.
A B
76
7.6. Supplementary File 6. Screening Experiments – Sample identifiers of analysed
microplate wells
Sample identifiers of both chlorophyll and lipid analyses of all days were given a unique sample number.
The respective sample numbers and sample IDs can be visualized in Table 7 and Table 8. The first
sample identifiers are related to the screened day of the level 2 and level 3 pools (D0 – Day 0; D1 – Day
1; D2 – Day2; D3 – Day 3); the second identifier stands for the gate applied for sorted (HC- High
Chlorophyll; LC – Low Chlorophyll; HL – High Lipids; LL – Low Lipids); the third identifier is the pool
identifier (A/B, C/D, E/F, G/H, Big Pool and Big Pool Diluted); and the fourth is the microplate well that
was analysed. Interesting phenotypes isolated on agarose plates are coloured in grey.
Table 7. Sample identifiers of microplate sorted-mutants screened for altered chlorophyll profiles.
Sample number Sample ID - Day 0 Sample ID - Day 1 Sample ID - Day 2
1 D0_HC_AB_B7 D1_HC_AB_A6 D1_HC_EF_F7
2 D0_HC_AB_C7 D1_HC_AB_C6 D2_HC_BP_B7
3 D0_HC_AB_D7 D1_HC_BP_A6 D3_HC_BPD_F7
4 D0_HC_AB_E7 D1_HC_BPD_B5 D3_HC_GH_D7
5 D0_HC_BP_B7 D1_HC_BPD_C5 D1_HC_AB_B7
6 D0_HC_BP_C8 D1_HC_CD_A6 D1_HC_AB_D7
7 D0_HC_BP_D7 D1_HC_CD_C6 D1_HC_AB_F7
8 D0_HC_CD_B7 D1_HC_AB_E6 D1_HC_BP_B7
9 D0_HC_CD_C7 D1_HC_BP_C6 D1_HC_BP_D7
10 D0_HC_CD_D7 D1_HC_BPD_E5 D1_HC_BP_F7
11 D0_HC_CD_C8 D1_HC_EF_A6 D1_HC_BPD_B7
12 D0_HC_EF_B7 D1_HC_EF_E6 D1_HC_BPD_D7
13 D0_HC_EF_C7 D1_HC_GH_A6 D1_HC_BPD_E7
14 D0_HC_EF_D7 D1_HC_GH_C6 D1_HC_CD_B7
15 D0_HC_GH_B7 D2_HC_BP_A6 D1_HC_CD_D7
16 D0_HC_GH_B8 D2_HC_BP_B6 D1_HC_CD_F7
17 D0_HC_GH_C7 D2_HC_BP_D6 D1_HC_EF_B7
18 D0_HC_GH_C8 D2_HC_BP_E6 D1_HC_EF_D7
19 D0_HC_GH_D7 D2_HC_BP_F6 D1_HC_GH_B7
20 D0_HC_GH_E7 D3_HC_AB_A6 D1_HC_GH_D7
21 D0_HC_BPD_E8 D3_HC_AB_E6 D1_HC_GH_F7
22 D0_HC_AB_B8 D3_HC_GH_A6 D2_HC_BP_C7
23 D0_HC_AB_D8 D3_HC_EF_A6 D2_HC_BP_D6
24 D0_HC_AB_F7 D3_HC_BPD_A6 D2_HC_BP_D7
25 D0_HC_AB_F8 D3_HC_BPD_C6 D2_HC_BP_E7
26 D0_HC_BPD_D7 D3_HC_CD_A6 D2_HC_BP_F7
27 D0_HC_BPD_E7 D3_HC_CD_C6 D3_HC_AB_B7
28 D0_HC_BP_E7 D3_HC_EF_C6 D3_HC_AB_D7
29 D0_HC_BP_B8 D1_HC_GH_B7 D3_HC_AB_F7
77
30 D0_HC_BP_C7 D1_HC_EF_D7 D3_HC_BPD_B7
31 D0_HC_BP_D8 D1_LC_BP_B8 D3_HC_BPD_D7
32 D0_LC_AB_C6 D1_LC_GH_B8 D3_HC_CD_B7
33 D0_LC_AB_F6 D2_LC_BP_D8 D3_HC_CD_D7
34 D0_LC_BP_A6 D3_LC_AB_E8 D3_HC_CD_F7
35 D0_LC_BPD_B5 D3_LC_BPD_B8 D3_HC_EF_B7
36 D0_LC_BP_B6 D3_LC_CD_B8 D3_HC_EF_D7
37 D0_LC_CD_B6 D3_LC_EF_B8 D3_HC_GH_B7
38 D0_LC_EF_B6 D3_LC_GH_B8 D3_HC_GH_F7
Table 8. Sample identifiers of microplate sorted-mutants screened for altered lipid profiles.
Sample ID Day 0 Day 1 Day 2
1 D0_HL_AB_C3 D1_HL_AB_A3 D2_HL_BP_D2
2 D0_HL_AB_C4 D1_HL_AB_C3 D2_HL_BP_E2
3 D0_HL_BP_A3 D1_HL_BPD_C3 D3_HL_BPD_B2
4 D0_HL_BP_A4 D1_HL_BPD_E3 D3_HL_BPD_D4
5 D0_HL_BP_B2 D1_HL_EF_A3 D3_HL_BPD_F2
6 D0_HL_BP_B3 D1_HL_GH_C3 D1_HL_BP_B2
7 D0_HL_BP_C3 D2_HL_BP_B3 D1_HL_BP_D2
8 D0_HL_BPD_A4 D2_HL_BP_C3 D1_HL_BP_F2
9 D0_HL_BPD_B2 D2_HL_BP_E3 D1_HL_BPD_B2
10 D0_HL_BPD_C3 D1_HL_AB_E3 D1_HL_BPD_D2
11 D0_HL_BPD_D2 D1_HL_BP_A3 D1_HL_BPD_E2
12 D0_HL_BPD_E3 D1_HL_BP_C3 D2_HL_BP_B2
13 D0_HL_BPD_F3 D1_HL_BP_E3 D2_HL_BP_C2
14 D0_HL_CD_B3 D1_HL_BPD_A3 D2_HL_BP_E4
15 D0_HL_CD_C2 D1_HL_CD_A3 D2_HL_BP_F2
16 D0_HL_EF_A4 D1_HL_CD_C3 D3_HL_AB_D2
17 D0_HL_EF_B2 D1_HL_CD_E3 D3_HL_AB_F2
18 D0_HL_EF_B4 D1_HL_EF_E3 D3_HL_CD_D2
19 D0_LL_AB_C1 D1_HL_GH_A3 D3_HL_CD_F2
20 D0_LL_BP_A1 D1_HL_GH_E3 D3_HL_EF_D2
21 D0_LL_BP_B1 D2_HL_BP_A3 D3_HL_EF_F2
22 D0_LL_BPD_B1 D2_HL_BP_D3 D3_HL_GH_D2
23 D0_LL_BPD_C1 D2_HL_BP_F3 D3_HL_GH_F2
24 D0_LL_BPD_F3 D3_HL_EF_E3
25 D0_LL_CD_B1
26 D0_LL_CD_C1
27 D0_LL_EF_C1
28 D0_LL_GH_B1
29 D0_LL_GH_C1
78
7.7. Supplementary File 7. Screening Experiments – Sample identifiers of isolated
events from microplate-wells
Table 9. Sample identifiers of the isolated events from previous microplate wells. The sample ID relates to the sample number of the microplate analyses. Screening of isolated cells was solely performed for Day0.
Sample ID Chlorophyll Day0 Sample ID Lipids Day0
28 D0_BP_E7 4 D0_BP_A4
3 D0_AB_D7 22 D0_BPD_B1
8 D0_CD_B7 25 D0_CD_B1
12 D0_EF_B7 26 D0_CD_C1
14 D0_EF_D7 27 D0_EF_C1
16 D0_GH_B8 17 D0_EF_B2
18 D0_GH_C8 ND ND
19 D0_GH_D7 ND ND
20 D0_GH_E7 ND ND
5 D0_BP_B7 ND ND